Document [original]

Title Page

Feasibility Analysis of Battery Electric Heavy-Duty

Trucks for Local Applications Based on Real

Usage Profiles Using the Example of a Catering

Lift Truck

vorgelegt von

Lamyaa Wdaah, M.Sc.

ORCID: 0000-0002-1462-3212

an der Fakultät V – Verkehrs- und Maschinensysteme

der Technischen Universität Berlin

zur Erlangung des akademischen Grades

Doktor der Ingenieurwissenschaften

- Dr.-Ing. -

genehmigte Dissertation

Promotionsausschuss:

Vorsitzender: Prof. Dr.-Ing. Henning Meyer

Gutachter: Prof. Dr.-Ing. Steffen Müller

Gutachter: Prof. Dr.-Ing. Ralph Pütz

Tag der wissenschaftlichen Aussprache: 23.01.2024

Berlin 2024

Acknowledgements

I would like to express my gratitude to all the individuals who assisted and supported me during

the preparation of this thesis. A special acknowledgment goes to my doctoral supervisor, Prof.

Dr.-Ing. Müller, the head of the Chair for Automotive Engineering at the Technical University

of Berlin, under whom I worked as a research assistant. I am grateful for the opportunity to

delve into my dissertation topic within such a captivating field of research and for his willingness

to discuss the subject and provide valuable suggestions and advice throughout the process.

I extend my thanks to all the contributors involved in the research project, eLift, which forms

an integral part of my dissertation. In particular, I would like to acknowledge Mr. Schmälzle

from Doll Fahrzeugbau GmbH and Mr. Pitsch from LSG Sky Chefs for their invaluable

assistance in providing the necessary information and enabling the analyses conducted in this

study.

Furthermore, I am appreciative of the collaboration, professional interactions, and pleasant

working environment shared with my former colleagues. Additionally, I am grateful to my

student assistants for their diverse involvement in this research, as well as the students whose

bachelor's and master's theses I supervised, as they made significant contributions to the

overall outcome of this work.

I would like to express my heartfelt thanks to my parents for their unwavering support, without

whom I would not have reached this point. I am also grateful to my siblings and friends for their

continuous encouragement, which helped me overcome challenges along the way. Finally, I

want to convey my deepest appreciation to my husband, Mohammed, for his constant

presence, dedication, patience, and understanding over the years.

I extend my sincere gratitude to all of you!

Lamyaa Wdaah

Abstract

Electrification has emerged as the optimal approach for achieving higher energy efficiency and

reduced emissions in the realm of road vehicles. While electrification is gaining ground in the

passenger car and light-duty vehicle sectors, it exhibits a lower degree of maturity in the heavy-

duty truck domain. One of the key factors contributing to this situation is the substantial initial

investment costs resulting from higher energy consumption and more demanding applications,

leading to markedly different economic viability conditions for electric heavy-duty trucks.

Conversely, electric vehicles offer the advantage of significantly lower energy consumption

and reduced maintenance expenses. Consequently, the economic feasibility of employing

electric heavy-duty trucks hinges on whether the higher initial investment costs can be offset

by lower operating expenditures, necessitating a comprehensive analysis of operational

profiles and working conditions to ascertain the truck's realistic energy consumption for a given

application. This analysis enables the prediction of whether electrifying the specific heavy-duty

truck represents a viable option. Furthermore, it is crucial to underscore other favorable

aspects of electric trucks, such as emissions and noise reduction, as well as enhanced

efficiency.

To address this matter, this thesis presents a simulation-based approach to analyze and

evaluate the electrification potential of a special-purpose heavy-duty truck employed in a

specific context. The study investigates the competitiveness of an electric prototype truck,

developed within a research project, in comparison to conventional diesel catering lift trucks

utilized at airports. Real individual usage profiles form the basis for the assessment. Initially,

the relevant operating profiles and work conditions pertaining to the catering lift trucks at

Frankfurt Airport are determined. Subsequently, a simulation model is employed to calculate

the energy consumption for complete work cycles, encompassing driving and operation of the

lifting system, utilizing recorded operational data. Based on the simulation results, an efficiency

analysis is conducted for both the driving and lifting systems, as well as the overall vehicle.

This is followed by a total cost of ownership analysis, which considers all costs associated with

the acquisition, operation, and disposal of the trucks, aiming to determine the economic

potential and cost differentials throughout their service lifespan. The life cycle environmental

impact of the considered catering lift trucks is also evaluated, with a focus on the aspects that

distinguish the environmental balance between the two truck technologies. Additionally, noise

emissions from both trucks are measured and assessed during driving and lifting system

operation.

The findings of this study demonstrate that the electric truck exhibits significant advantages in

terms of efficiency improvement and consumption reduction compared to the conventional

diesel truck. The total cost of ownership analysis reveals that while the electric truck entails

substantial incremental acquisition costs, it compensates for this with notable benefits in

operational expenses and residual value as compared to the diesel truck. However, the

economic operation and profitability of the electric truck are heavily contingent on the

acquisition costs and battery prices.

The comparison of the environmental impact between the two trucks indicates that, even when

utilizing the current German electricity mix, the electric truck demonstrates a superior

environmental footprint in comparison to the diesel truck. This advantage is further enhanced

when exclusively utilizing electricity from renewable energy sources. Lastly, the acoustic

measurements comparison demonstrates that the electric truck generates lower noise levels

than the diesel truck, both during driving and lifting system operation.

VII

Kurzfassung

Die Elektrifizierung hat sich als idealer Weg zu höherer Energieeffizienz und weniger

Emissionen für Straßenfahrzeuge erwiesen. Während sich die Elektrifizierung im Segment der

Personenkraftwagen und der leichten Nutzfahrzeuge bereits auszubreiten beginnt, zeigt sie

im Segment der schweren Lastkraftwagen (Lkw) einen geringeren Reifegrad. Einer der

wichtigsten Gründe dafür sind die hohen Anfangsinvestitionskosten aufgrund der höheren

Energieverbrauch und schwereren Anwendungen, die deutlich andere Rahmenbedingungen

für die Wirtschaftlichkeit elektrischer schweren Lkw bedeuten. Andererseits haben

Elektrofahrzeuge den Vorteil eines deutlich geringeren Energieverbrauchs und geringerer

Wartungskosten. Folglich könnte sich je nach Anwendung der Einsatz eines elektrischen

schweren Lkw wirtschaftlich lohnen, wenn die höheren Anfangsinvestitionskosten durch die

geringeren Betriebskosten kompensiert werden können. Dies erfordert eine gründliche

Analyse der Betriebsprofile und Arbeitsbedingungen, um den realistischen Energieverbrauch

des Lkw für den spezifischen Einsatz abzuleiten. Dementsprechend lässt sich prognostizieren,

ob die Elektrifizierung des betreffenden schweren Lkw eine tragfähige Option wäre. Darüber

hinaus gilt es, weitere positive Aspekte von elektrischem Lkw hervorzuheben, wie etwa die

Reduzierung von Emissionen und Lärm oder die Steigerung der Effizienz.

Vor diesem Hintergrund wird in dieser Dissertation ein simulationsbasierter Ansatz zur Analyse

und Bewertung des Elektrifizierungspotenzials eines schweren Lkw mit Sondereinsatz

vorgestellt. Die Wettbewerbsfähigkeit eines in einem Forschungsprojekt entwickelten Elektro-

Prototyps wird im Vergleich zu konventionellen dieselbetriebenen Catering-Hubfahrzeuge, die

auf Flughäfen eingesetzt werden, anhand realer individueller Nutzungsprofile untersucht.

Dazu werden zunächst die für die am Flughafen Frankfurt eingesetzten Catering-

Hubfahrzeuge relevanten Einsatzprofile und Arbeitsbedingungen ermittelt. Anhand eines

Simulationsmodells wird auf Basis der erfassten realen Betriebsdaten den Energieverbrauch

für komplette Arbeitszyklen inklusive Fahren und Bedienen des Hubsystems ermittelt.

Basierend auf den Simulationsergebnissen wird eine Effizienzanalyse individuell für die

Antriebs- und Hubsysteme sowie für das Gesamtfahrzeug durchgeführt. Anschließend wird

eine Gesamtbetriebskosten- (Total Cost of Ownership) Analyse durchgeführt, um das

wirtschaftliche Potenzial und die Kostenunterschiede zwischen den beiden Lkw über die

gesamte Lebensdauer zu ermitteln, wobei alle Kosten berücksichtigt werden, die mit der

Anschaffung, dem Betrieb und der Verwertung des Lkw verbunden sind. Auch die

Lebenszyklus-Umweltauswirkungen der betrachteten Catering-Hubfahrzeuge werden

bewertet, wobei der Schwerpunkt auf den Aspekten liegt, die einen Unterschied in der

Umweltbilanz zwischen den beiden Lkw-Technologien ausmachen. Zusätzlich werden die

VIII

Geräuschemissionen beider Lkw gemessen und bewertet sowohl für das Lkw-Vorbeifahren

als auch für den Betrieb des Hubsystems.

Die Arbeitsergebnisse zeigen, dass der Elektro-Lkw einen großen Vorteil hinsichtlich

Effizienzsteigerung und Verbrauchsreduzierung gegenüber dem konventionellen Diesel-Lkw

hat. Die Gesamtbetriebskosten-Analyse zeigt, dass der Elektro-Lkw zwar sehr hohe

inkrementelle Anschaffungskosten hat, aber gegenüber dem Diesel-Lkw deutliche Vorteile bei

den Betriebskosten sowie beim Restwert hat. Die Wirtschaftlichkeit und Rentabilität des

Elektro-Lkw hängen jedoch maßgeblich von den Anschaffungskosten und Batteriepreisen ab.

Der Vergleich der Umweltauswirkungen der beiden Lkw zeigt, dass der Elektro-Lkw auch bei

Verwendung des heutigen deutschen Strommixes eine bessere Umweltbilanz im Vergleich

zum Diesel-Lkw aufweist. Der Vorteil des Elektro-Lkw wird verstärkt, wenn ausschließlich

Strom aus erneuerbaren Energiequellen verwendet wird. Abschließend zeigen die

Vergleichsergebnisse der Akustikmessungen, dass der Elektro-Lkw sowohl beim Fahren als

auch beim Bedienen des Hubsystems weniger Lärm verursacht als der Diesel-Lkw.

IX 
 
Table of Contents 
Abstract ..................................................................................................................... V 
Kurzfassung ............................................................................................................ VII 
Table of Contents .................................................................................................... IX 
List of Symbols ........................................................................................................ XI 
List of Abbreviations .............................................................................................. XV 
Introduction ........................................................................................................ 1 
1 Motivation ............................................................................................................ 1 
2 State of the Art ..................................................................................................... 3 
3 Research Questions and Objectives ...................................................................18 
4 Procedure of the Work ........................................................................................20 
5 Outline of the Work .............................................................................................21 
Reference Trucks and Usage Profile.............................................................. 24 
1 Catering Lift Trucks .............................................................................................24 
2 Operating Profile and Working Conditions ..........................................................27 
3 Representative Work Cycles and Measurement Datasets ..................................28 
Modeling and Simulation of the Catering Lift Truck ..................................... 38 
1 Driving System Model .........................................................................................38 
2 Lifting System Model ..........................................................................................50 
Efficiency and Potential Analysis ................................................................... 61 
1 Driving System ...................................................................................................61 
2 Lifting System .....................................................................................................64 
3 Entire Vehicle .....................................................................................................66 
Economic Analysis Based on the Total Cost of Ownership (TCO) ............. 69 
1 Calculation Method .............................................................................................69 
2 Definition of the Operating Scenarios ..................................................................72 
3 Important Influencing Factors on the TCO Analysis ............................................74 
4 Cost Elements ....................................................................................................80 
5 Comparison of the TCO of Conventional and Electrified Catering Lift Trucks ......84 
6 Sensitivity Analysis .............................................................................................92 
Ecological Analysis and Environmental Impact ........................................... 99 
1 Emission Parameters ........................................................................................ 100 
2 Results of the Life Cycle Analysis (LCA) ........................................................... 105 
3 Sensitivity Analysis ........................................................................................... 107 
Noise Emission Measurement ...................................................................... 109 

X 
 
7.1 Measurement and Assessment Parameters ..................................................... 109 
7.2 Measurement Equipment and Logging Data ..................................................... 111 
7.3 Measurement Concept...................................................................................... 111 
7.4 Measurement Results ....................................................................................... 114 
8 Possible Improvements for the Lifting System ........................................... 121 
8.1 Reduce Pressure Losses .................................................................................. 121 
8.2 Recovery of Potential Energy............................................................................ 123 
9 Summary and Outlook ................................................................................... 126 
9.1 Summary .......................................................................................................... 126 
9.2 Outlook ............................................................................................................. 128 
Appendix ............................................................................................................... 130 
A – Technical Specifications of the Considered Catering Lift Trucks ............................... 130 
B – Data Processing of the Driving Cycles ...................................................................... 132 
C – Driving Cycles Recorded with the Diesel Truck ........................................................ 135 
D – Impact of Payload Variation on Energy Efficiency Analysis ...................................... 137 
E – Acoustic Analyzer Used for the Noise Emission Measurements ............................... 138 
Bibliography .......................................................................................................... 139 
List of Figures ....................................................................................................... 152 
List of Tables ........................................................................................................ 155 
Own Publications ................................................................................................. 157 
Supervised Student Work .................................................................................... 158 
 
 

List of Symbols

Symbol

Description

Unit

𝐴

vehicle cross-sectional area

𝐴𝐶𝑡0

vehicle acquisition costs at the current year

€

𝐴𝑙𝑖𝑛𝑒

pipeline cross-sectional area

𝐴𝑃𝐴

cylinder piston area on side A

𝐴𝑃𝐴1,2

cylinder piston area on side A of stage 1 or 2 of the

telescopic cylinder

𝐴𝑃𝐵

cylinder piston area on side B

𝑎

vehicle acceleration

m/s2

𝐵𝑅𝐶𝑡𝑏

battery replacement cost in year 𝑡𝑏

€

𝑏𝑎𝑡𝑆𝑂𝐶

battery state of charge

𝐶𝑎𝑝𝑠𝑡𝑎𝑟𝑡

available battery capacity at the start of the rout

kWh

𝐶𝑎𝑝𝑡𝑜𝑡𝑎𝑙

total available capacity of the fully charged battery

kWh

𝐶𝐶𝑡

energy or fuel consumption costs

€

𝐶𝑜𝑛𝑠𝑓𝑢𝑒𝑙

total fuel consumption of the diesel truck

𝐶𝑜𝑛𝑠𝑠𝑝𝑒𝑐

engine specific fuel consumption

g/kWh

𝑐

coefficient of the viscous friction force

kg/s

𝑐𝑠

Stribeck velocity

m/s

𝑐𝑤

drag coefficient

𝑑𝑙𝑖𝑛𝑒

pipeline inner diameter

𝐸𝑖𝑛

input energy

kWh

𝐸𝑖𝑛𝑑

total energy consumption of the diesel truck

kWh

𝐸𝑖𝑛𝑒

total energy consumption of the electric truck

kWh

𝐸𝑜𝑢𝑡

output energy

kWh

𝐹𝑎

air resistance force

𝐹𝑎𝑐𝑐

acceleration resistance force

𝐹𝑎𝑐𝑐𝑡𝑟𝑎𝑛

translational acceleration resistance force

𝐹𝑎𝑐𝑐𝑟𝑜𝑡

rotational acceleration resistance force

𝐹𝐶

Coulomb friction force

𝐹𝐷

speed-dependent damping force

𝐹𝑔

gradient resistance force

XII

Symbol

Description

Unit

𝐹𝐿

external load force on the cylinder piston

𝐹𝑛

normal force of the cylinder piston

𝐹𝑅

cylinder Coulomb friction force

𝐹𝑟

rolling resistance force

𝐹𝑆

stiction friction force

𝐹𝑤

total driving resistance force

𝑓𝑟

rolling resistance coefficient

𝑔

gravitational acceleration

m/s2

𝐻𝑢

diesel heating value

kWh/L

ℎ𝑜𝑖𝑙

height of the hydraulic oil

𝐼𝐶𝑡

vehicle insurance costs

€

𝐼𝐹𝐶𝑡0

infrastructure acquisition costs at the current year

€

𝑖

annual interest rate

𝑖𝑑𝑖𝑓𝑓

differential ratio

𝑖𝑔𝑒𝑎𝑟

auxiliary gear ratio

𝑖𝑡𝑟𝑎𝑛𝑠

transmission gear ratio

𝐾

bulk modulus

GPa

𝐿𝐴𝐸

exposure of A-weighted sound pressure level

𝐿𝐴𝑒𝑞

equivalent continuous A-weighted sound pressure level

𝐿𝐴𝐹𝑚𝑎𝑥

maximum A-weighted and F-weighted sound pressure

level

𝐿𝐸

sound exposure level

𝐿𝑒𝑞

equivalent continuous sound pressure level

𝐿𝑚𝑎𝑥

maximum sound pressure level

𝐿𝑍𝑒𝑞

equivalent continuous Z-weighted sound pressure level

𝑙𝑙𝑖𝑛𝑒

pipeline length

𝑀

pump absorbed torque

𝑀𝐶𝑡

vehicle maintenance costs

€

𝑚𝐿

load mass

𝑚𝑃

cylinder piston mass

𝑚𝑃1,2

cylinder piston mass of stage 1 or 2 of the telescopic

cylinder

XIII

Symbol

Description

Unit

𝑚𝑣

total vehicle mass

𝑛

motor rotational speed

1/min

𝑃𝑖𝑛𝑑

total power demand of the diesel truck

𝑃𝑖𝑛𝑒

total power demand of the electric truck

𝑃𝑚𝑜𝑡𝑜𝑟

motor output power

𝑃𝑜𝑢𝑡

output power

𝑝𝐴

pressure in the pipeline A

bar

𝑝𝐵

pressure in the pipeline B

bar

𝑝ℎ𝑖

road angle of inclination

𝑝𝐿𝑆

pressure losses due to the load-sensing function

bar

𝑝𝑙𝑖𝑛𝑒

pressure losses in the pipelines

bar

𝑝𝑃𝑢𝑚𝑝

pressure at the pump outlet

bar

𝑝𝑝𝑜𝑡

pressure losses due to gravity

bar

𝑄𝐴

volumetric flow rate in cylinder chamber A

L/min

𝑄𝐵

volumetric flow rate in cylinder chamber B

L/min

𝑄𝑡𝐴

total change of the volumetric flow rate in cylinder

chamber A

L/min

𝑄𝑡𝐵

total change of the volumetric flow rate in cylinder

chamber B

L/min

𝑄𝑉𝐴

volumetric flow rate difference due to volume change in

cylinder chamber A

L/min

𝑄𝑉𝐵

volumetric flow rate difference due to volume change in

cylinder chamber B

L/min

𝑅𝑉𝑇

vehicle residual value at the end of its service life

€

𝑟𝑑𝑦𝑛

vehicle wheel radius

𝑇

service life duration of the truck

𝑇𝐶𝑂

present value of the TOC

€

𝑇𝐶𝑡

vehicle tax costs

€

𝑇𝑚𝑜𝑡𝑜𝑟

motor output torque

𝑇𝑤

vehicle wheel torque

𝑡

future calculation year

𝑡0

time point (year) of calculation

𝑡𝑏

year of the battery replacement

XIV

Symbol

Description

Unit

𝑉0𝐴

initial volume of cylinder chamber A

𝑉0𝐴1,2

initial volume of cylinder chamber A of stage 1 or 2 of the

telescopic cylinder

𝑉0𝐵

initial volume of cylinder chamber B

𝑉𝐴

volume of cylinder chamber A

𝑉𝐵

volume of cylinder chamber B

𝑣

vehicle driving speed

m/s

𝑥𝑃

stroke of the cylinder piston

𝑥𝑝_𝑚𝑎𝑥

maximum stroke of the cylinder piston

𝑥𝑃

󰇗

velocity of the cylinder piston

m/s

𝑥𝑃

󰇘

acceleration of the cylinder piston

m/s2

Greek Symbol

Description

Unit

𝜂𝐴𝐶/𝐷𝐶

efficiency of the power electronics

𝜂𝑑𝑖𝑓𝑓

efficiency of the differential

𝜂𝐸

energy efficiency

𝜂𝐸𝑛𝑒𝑤

energy efficiency of a new drive

𝜂𝐸𝑟𝑒𝑓

energy efficiency of the reference drive

𝜂𝑔𝑒𝑎𝑟

efficiency of the auxiliary gear

𝜂𝑚𝑜𝑡𝑜𝑟𝑒

efficiency of the electric motor

𝜂𝑡𝑟𝑎𝑛𝑠

efficiency of the transmission

Δ𝜂𝐸

efficiency comparison index

𝜆

rotational mass addition factor

𝜇𝑘

sliding friction coefficient

𝜇𝑜𝑖𝑙

dynamic viscosity of the hydraulic oil

Ns/m2

𝜇𝑠

stiction friction coefficient

𝜔𝑚𝑜𝑡𝑜𝑟

motor output angular velocity

rad/s

𝜔𝑤

vehicle wheel angular velocity

rad/s

𝜌𝑎

air density

kg/m3

𝜌𝑑𝑖𝑒𝑠𝑒𝑙

diesel density

kg/L

𝜌𝑜𝑖𝑙

density of the hydraulic oil

kg/m3

List of Abbreviations

ACEA

European Association of Automobile Manufacturers (Association des

Constructeurs Européens d'Automobiles)

BDEW

German Association of Energy and Water Industries (Bundesverband der

Energie- und Wasserwirtschaft)

BMDV

German Federal Ministry for Digital and Transport (Bundesministerium für

Digitales und Verkehr)

BMF

German Federal Ministry of Finance (Bundesministerium der Finanzen)

BMJ

German Federal Ministry of Justice (Bundesministerium der Justiz)

BMWK

German Federal Ministry for Economic Affairs and Climate Action

(Bundesministerium für Wirtschaft und Klimaschutz)

CO2

Carbon Dioxide

CO2-eq

Carbon Dioxide Equivalent

DoD

Depth of Discharge

European Commission

EEG

Renewable Energy Act (Erneuerbare-Energien-Gesetz)

European Union

UBA

Federal Environment Agency (Umweltbundesamt)

GHG

Greenhouse Gas

IINAS

International Institute for Sustainability Analyses and Strategies (Internationales

Institut für Nachhaltigkeitsanalysen und-strategien)

LCA

Life Cycle Analysis

PED

Primary Energy Demand

SOC

State of Charge

TCO

Total Cost of Ownership

TTW

Tank to Wheel

VAT

Value Added Tax

WTT

Well to Tank

WTW

Well to Wheels

1 Introduction

1.1 Motivation

The escalating pace of human activities is contributing to a surge in air pollution levels. The

increased concentration of greenhouse gases (GHGs) in the atmosphere, primarily resulting

from the combustion of fossil fuels, leads to the retention of a significant amount of the sun's

heat, contributing to global warming—a phenomenon with multifaceted humanitarian

consequences [UBA, 2022] [Rüdiger, 2018]. To address these challenges, the European

Union (EU) has established targets to mitigate carbon dioxide (CO2) emissions. Building upon

the principles of the Green Deal, presented by the European Commission (EC) in 2019, the

EU aims to achieve climate neutrality, meaning zero-net GHG emissions, by 2050. As an

interim milestone, the Green Deal sets a target of at least 55% reduction in GHG emissions by

2030 compared to 1990 [EC, 2019b]. Additionally, the Paris Agreement, ratified by numerous

countries including Germany, seeks to limit the global average temperature increase to well

below two degrees Celsius above pre-industrial levels [EC, 2016].

Within Europe, the road transport sector significantly contributes to GHG emissions and,

consequently, climate change. In 2019, the road transport sector accounted for approximately

one-fifth of the total EU GHG emissions, with heavy-duty vehicles, including trucks and buses,

contributing to around 27% of this figure [EP, 2019]. Notably, in Germany, the transport sector

ranks as the third-largest emitter of GHGs after the energy and industry sectors, with road

traffic constituting the primary source of emissions. The transport sector was responsible for

around 20% in 2019 and 19% in 2021 of Germany's total GHG emissions, with heavy vehicles,

including trucks and buses, accounting for 35% and 38% of this portion, respectively [BMUV,

2020] [BMUV, 2022].

Against this backdrop, mitigating GHG emissions in the transport sector assumes critical

significance in attaining local and global climate objectives. In Europe, the EU has established

CO2 emissions reduction targets for the transport sector, which have been extended to

encompass heavy-duty vehicles. According to Regulation (EU) 2019/1242, manufacturers of

heavy-duty trucks are mandated to reduce CO2 emissions from newly sold vehicles by 15% by

2025 and by 30% by 2030 compared to 2019/2020 levels [EU, 2019] [EC, 2019a]. Failure to

comply with these regulations will result in financial penalties for manufacturers [Rodríguez,

2019]. Similar regulations have also been adopted in the United States of America (USA)

through California's advanced clean truck regulations [CARB, 2021]. Consequently, truck

manufacturers are compelled to explore alternative driving options to meet these objectives

and avoid hefty fines.

Battery electric trucks are regarded as the most promising zero-emission truck technology that

has been explored in studies and research projects. These vehicles offer high efficiency,

leading to energy savings and reduced operating costs compared to conventional trucks.

Additionally, the elimination of internal combustion engines contributes to lower maintenance

expenses. Given the heavy-duty truck sector's substantial fuel consumption, there is a

favorable starting point for the adoption of electric vehicles. However, the initial development

and implementation of new technologies typically result in additional costs, making battery

electric heavy-duty trucks currently more expensive than their conventional counterparts,

primarily due to battery costs. This cost disparity discourages fleet operators, particularly those

for whom purchase costs constitute a significant portion of total cost of ownership (TCO), from

incorporating electric trucks into their fleets. To facilitate economic use, many governments

provide subsidies to make electric vehicles, including heavy-duty trucks, more affordable [IEA,

2019]. Once battery prices decline, battery electric heavy-duty trucks will become cost-

competitive or even cheaper than conventional trucks. At that stage, governments can

discontinue subsidies, and the widespread adoption of battery electric heavy-duty vehicles will

naturally take off [Basma et al., 2021] [Unterlohner, 2021] [Mareev, 2018].

Despite these challenges, there is considerable interest in the rapid proliferation of battery

electric trucks from governments, manufacturers, and users [IEA, 2020a]. Numerous

prototypes of battery electric heavy-duty trucks have already been developed, and their

applicability in commercial fleets is being examined in various projects. However, most studies

focus on long-distance applications, with limited coverage of distribution trucks and other local

uses.

Meanwhile, fleet operators are increasingly questioning the sustainable economic viability of

battery electric heavy-duty trucks in their specific applications, given rising cost pressures and

uncertainties surrounding these vehicles. These uncertainties pose significant barriers to the

market's rapid expansion and the decarbonization of the heavy truck sector. Therefore,

investigating the economically viable applications for battery electric heavy-duty trucks is

crucial, in addition to the support provided for their purchase.

Furthermore, despite the existing obstacles, other factors such as zero emissions in urban

areas, reduced noise pollution, and potential traffic diversion may incentivize commercial

customers to transition to battery electric heavy-duty trucks [IEA, 2020a].

In light of these considerations, conducting a comprehensive analysis of all relevant factors,

including usage profiles and contextual conditions, throughout the truck's lifespan is essential

to ascertain the advantages of a battery-powered electric truck in a specific application.

1.2 State of the Art

1.2.1 Electrification of Heavy-Duty Trucks

The emergence of the tightening emissions regulations, combined with the limited availability

of fossil fuel oil and high fuel costs, has increased the demand for alternative driving solutions.

Electric mobility is a smart solution to meet these challenges and achieve efficient and

environmentally friendly transportation, especially with the use of renewable energies. Electric

vehicles powered by electricity from renewable energy sources are almost emission-free and

produce no exhaust fumes. Moreover, the electrification of the powertrain yields a significant

increase in the efficiency of the vehicle and thus a lower primary energy consumption [Kampker

et al., 2013].

Promising zero-emission technologies for the electrification of heavy-duty trucks include those

that rely on clean electricity from renewable sources, either directly through electricity, such as

battery electric trucks and catenary electric trucks, or indirectly using electricity-based fuels,

such as fuel cell (hydrogen) electric trucks and e-fuel (electric fuel) trucks [Moultak et al., 2017]

[Mareev, 2018] [Unterlohner, 2021]. All these technologies have no exhaust emissions, and

this eliminates local CO2 and air pollutant emissions. However, each of these technologies has

its own technical, cost, and infrastructure requirements, and therefore appropriate applications.

The direct electrification technologies have an electric powertrain that offers advantages over

a conventional powertrain. For domestic applications, battery-powered electric trucks are most

appropriate as they are the most economical and easiest to provide charging infrastructure as

it is already fairly widely available [Mareev, 2018] [Unterlohner, 2021]. Although catenary

trucks technology has the advantage of high efficiency which is superior to that of battery

trucks, the implementation of the infrastructure in the roadway and the associated maintenance

require extensive work and high costs, which make it not suitable for local applications. They

are intended for applications with long distance transportation and large freight loads

[Siemens, 2012] [Moultak et al., 2017] [Wickert et al., 2018].

The indirect electrification technologies are characterized by a high energy density and

therefore provide a long driving range, but on the other hand they have the disadvantage of

very low efficiency, as they require a lot of electricity, which puts additional strain on the power

supply and are likely to cost more compared to direct electrification technologies [Unterlohner,

2021] [Ambel & Earl, 2017]. Although fuel cell trucks also have an electric powertrain and often

a traction battery to store braking energy, a great deal of energy is lost when hydrogen is

produced based on electricity and converted back into electricity in the vehicle [Göckeler et al.,

2020]. In e-fuel trucks, gaseous or liquid fuel produced on the basis of renewable electricity is

used to power a combustion engine, which has very low efficiency [Unterlohner, 2021].

In general, all electrified driving technologies for heavy-duty trucks are still in the early stages

of the technical and economic learning curve and are therefore not yet cost-effective when

compared to conventional diesel trucks. In the field of long-distance road transport, it is still

uncertain which technology will prevail. The transition to electric vehicles in these applications

still presents a particular challenge, as trucks that must cover long distances and at the same

time achieve high average speeds require high energy reserves, especially in light of the

current lack of charging stations on the highways. However, for urban and local applications,

battery powered electric trucks are suitable for everyday use and may be very attractive to

many customers as they are becoming increasingly commercially available. The electric motor

with the recuperation of braking energy enables the benefit of the frequent starting and

stopping phases in the driving profile to be utilized as there is already a significant amount of

braking energy that can be recovered and stored in the onboard storage. The stored energy

can both be used as a driving force as well as to deliver power to the auxiliary devices. It is

assumed in much of the literature that battery electric trucks will be the most technically and

overall cost-effective technology in the future as, given the technical and economic

developments, they are expected to be on par with diesel trucks in the near future [Unterlohner,

2021] [Göckeler et al., 2020] [Mareev, 2018].

1.2.2 Competitiveness of Battery Electric Heavy-Duty Trucks

1.2.2.1 Energy Efficiency and Consumption

Efficiency and final primary energy demand are of the most important parameters when

comparing competitive alternative vehicles to ensure the effective economic and

environmental operation of a vehicle. Direct electrification techniques are the most energy

efficient among all other technologies, and thus the least demanding of primary energy.

Therefore, battery electric vehicles have gained great popularity in the sectors of passenger

cars and light-duty vehicles, which has led to their widespread use in recent years. Likewise,

heavy-duty trucks are currently being converted to battery electric trucks and are expected to

increasingly penetrate the market in the coming years [Unterlohner, 2021] [Jöhrens et al.,

2021].

When analyzing the energy efficiency of a vehicle, in addition to the energy conversion

efficiency in the vehicle, which is called tank to wheel (TTW), the efficiency of energy

production and processing, which is called well to tank (WTT), must also be taken into account.

In the literature discussing the energy efficiency of battery electric heavy-duty trucks, it is often

compared to some other technologies with reference to diesel trucks. Battery electric trucks

cause the least losses both in the energy conversion in the vehicle and the provision of energy

and thus they have the highest overall efficiency from the well to wheels (WTW) among other

truck technologies [Mareev, 2018] [Göckeler et al., 2020].

Since the drive is the central consumer of energy in a vehicle, the efficiency of a vehicle mainly

depends on the efficiency of its drive. The higher efficiency of electric motors compared to

internal combustion engines, as well as the use of regenerative braking, are the main reasons

why electric vehicles are more efficient, whereby the efficiency of electrical motors ranges

around 90% depending on the design. By comparison, the efficiency of diesel engines is only

approximately 40% [Raiber et al., 2014]. This large variation in the efficiency of the drive

technologies leads to a big difference in energy consumption while another great advantage

of the electric motor is that it can be switched into alternator mode when braking and use the

braking energy to charge the battery. This recovery significantly reduces energy consumption

and thus enhances both the efficiency and range of the vehicle. This advantage is especially

evident in inner-city driving with many stops and low average speeds [Gräbener, 2017]. The

study by [Unterlohner, 2021] examines all technologies that can decarbonize Germany's long-

haul truck fleet based on the use of renewable electricity and concludes that the total efficiency

of the direct electrification technology is at least twice that of the renewable hydrogen

technology and more than three times the efficiency of liquid or gaseous e-fuels and diesel

technologies.

1.2.2.2 Economic Considerations

Cost criteria are some of the most important criteria when deciding to purchase a truck. In

addition to purchasing costs, operating costs also play an important role. Therefore, the truck's

economic viability should be evaluated based on a TCO analysis, which also allows for a

comparison of alternative investment options. In the car and light-duty vehicle segments, such

TCO tools may already be available as a service from the manufacturer or as a commercial

offer [Jerratsch, 2021], whereas they are currently not provided in the same format in the case

of heavy-duty vehicles, especially for battery electric trucks. However, some cost calculators,

e.g. the AFLEET tool (Alternative Fuel Life-Cycle Environmental and Economic

Transportation), can be used for comparative calculations of battery electric, diesel, and other

alternative drive technologies trucks [ANL, 2023]. On the other hand, investigating the TCO for

alternative vehicle technologies has recently become the subject of extensive research and

analysis and several studies have analyzed the TCO of heavy-duty trucks for different

applications, especially in the EU and the USA Alternative truck technologies are often

compared to conventional diesel trucks on the basis of their economic performance. However,

because inputs to the TCO analyses vary based on the application and where the truck is

operated, the results of the analyses are case-related and cannot be generalized.

The TCO analysis includes all costs incurred for the purchase of the truck, the costs for its use

over the entire ownership period, and its resale value. The capital value method is used to

calculate the present value of all costs incurred during the holding period. For this purpose, the

value of future payments is calculated in relation to the current point in time, assuming a

specified interest rate. This allows a comparison of the life cycle costs of two different vehicles

for the same holding period where the main result is the differences in the net present values

of the total costs of the two alternative vehicles [Mareev, 2018]. The TCO analysis will be

discussed in more detail in Chapter 5.

The comprehensive cost calculation is necessary to compare the economic evaluation of

battery electric trucks versus diesel trucks as these differ significantly in terms of their purchase

and operating costs. Although purchase prices are still high, electric trucks are generally more

energy efficient compared to internal combustion trucks, and therefore cheaper to operate. In

addition, electric trucks can also achieve savings in some other operating costs such as

maintenance costs and vehicle tax. On the other hand, there are also concerns about battery

service life and vehicle resale value and thus it is necessary to consider all of the TCO factors

that differ between battery electric trucks and diesel trucks by taking the framework conditions

into account. The factors that constitute the major cost differences between battery truck

technology and diesel truck technology are discussed individually below.

Vehicle battery

Battery electric trucks typically have higher purchase costs than diesel trucks, primarily due to

the cost of batteries. The battery accounts for the largest share of the surcharge for the electric

vehicle compared to a comparable vehicle with a combustion engine. The impact of other drive

components, such as the motor, inverter, and controller on the overall vehicle costs is not as

significant. The cost of the battery pack mainly depends on the unit cost (per kWh) and this

varies depending on the chemistry of the battery. As a result, the size and type of battery

determine its cost, and these are chosen based on the energy requirements of a particular

vehicle. Therefore, the battery pack costs for heavy-duty vehicles tend to be higher due to the

bigger sizes and different requirements and working conditions. In addition to the price of the

battery, battery life is especially important. If the battery has a short life, it may have to be

replaced during the life of the truck, which can significantly increase the life cycle costs.

Infrastructure

In the case of battery electric trucks, apart from the costs of purchasing, TCO calculations

should also take any necessary infrastructure costs into account. These may include, in

addition to the purchasing costs of the charging station, other costs such as those associated

with construction and installation, municipal permit costs, etc. In addition, annual maintenance

costs must also be considered. These are usually assumed as a percentage of the initial

investment costs. If the charging station is able to accommodate multiple vehicles, the costs

are divided between the number of vehicles [Den Boer et al., 2013] [Jöhrens et al., 2021].

Infrastructure requirements vary widely depending on the vehicle battery type and size, and

the duty cycle. For example, trucks that travel long distances and have larger payloads usually

contain large batteries, and therefore require higher capacity charging stations to

accommodate their operation cycles. Thus, charging stations have the potential to add

significant costs (up to 10–25%) to the total cost of battery electric trucks in terms of the cost

of ownership of the truck [Hall & Lutsey, 2019].

Energy cost

The fuel or energy costs of vehicles are due to their consumption and current and future fuel

or electricity prices. The vehicle consumption is usually calculated using a vehicle simulation

model and pre-defined operating scenarios whereby the fuel and electricity prices consist of

the costs for providing the fuel or electricity, which vary according to the supplier, in addition

to the added duties and taxes. There are significant differences in the prices of electrical

energy, depending on the supplier as well as the charging type and time. For example,

charging during the night is more beneficial, since electricity rates are cheaper. On the other

hand, future prices are subject to a great deal of uncertainty because they are based on

predictions. While various forecasts can be found in the literature, they all concur that

significant increases in diesel prices until 2030 can be expected due to higher taxes, fees, and

the CO2 offset tax imposed on diesel. In comparison, the forecasts also expect an increase in

electricity costs until 2030, although these are expected to be relatively moderate. Generally,

the costs of electricity from the grid have historically not been as volatile as those of diesel fuel

which is expected to increase the cost advantage of battery electric trucks.

Due to the driving efficiency, the energy costs of the electric battery truck are advantageous.

In comparison, fuel costs are the biggest cost factor for diesel trucks due to their lower

powertrain efficiency [Mareev, 2018].

Maintenance

Although the electric truck industry is still at an early stage of development and field experience

is limited so far, these trucks certainly have lower long-term maintenance costs compared to

diesel trucks. This is due to several reasons, including reduced brake wear resulting from

regenerative braking, engine oil elimination, and the simplicity of the electric powertrain

compared to that of an internal combustion engine. Battery electric trucks also require lower

maintenance costs compared to fuel cell trucks. Although there is considerable variation in the

literature regarding the estimation of maintenance costs for electric trucks, in general, the

assumed savings in maintenance costs can be assessed as being approximately one-third

compared to that of diesel trucks [Den Boer et al., 2013] [Kleiner & Friedrich, 2017].

Residual value

The residual value of a heavy-duty truck may play an important role in calculating the TCO,

especially for vehicles with a short ownership period by the first user. Evaluating the residual

value of a truck is based on several criteria such as, among others, the gross vehicle weight,

powertrain type, technology maturity, vehicle purchase price, total vehicle mileage, and

infrastructure availability [Kleiner & Friedrich, 2017]. In particular, the resale value of a battery

electric truck is subject to a great deal of uncertainty and cannot easily be predicted given the

early stage of the market as there is currently no used vehicle market for battery electric trucks.

The range of models available in the market are mostly prototypes, not serial products,

although this is likely to change soon due to the rapid development of battery technologies.

However, expectations about future prices in the used vehicle market cannot be quantified for

battery electric heavy-duty trucks from today's perspective. Furthermore, reducing the prices

of new vehicles through subsidies may have an impact on the used vehicle market in the future

[Jöhrens et al., 2021]. On the other hand, since the battery is the most valuable component of

any battery electric vehicle, battery aging and replacement are important factors in deriving the

resale value of a battery electric truck as the battery alone can provide a residual value to the

owner regardless of the residual value of the vehicle itself. At the end of its usual service life,

the used battery pack can be further used as a stationary energy storage system, for example

for energy generated by solar panels, or it can be recycled. The loss in the battery value is

usually determined by using the capacity loss due to the aging of the battery [Hill et al., 2019].

Payback period and cost parity

When factoring in all costs of ownership from the vehicle purchase, and infrastructure and

operating costs, in addition to the current taxes, fees, tolls, and subsidies, the payback period

for the battery electric truck can be calculated. The payback period reflects the minimum period

of ownership so that the additional purchase costs of the truck are offset by savings in

operating costs. Consequently, the payback period depends on the acquisition costs as well

as on the operating profiles. Applications in which the incremental cost is low and operating

period savings are high will have a short payback period, while applications with very high

incremental costs and modest operating period savings will have a long payback period that

may exceed the expected life of the vehicle. For example, a higher economic potential appears

in applications of long-haul trucks with higher annual mileage and fast charging possibilities

than in applications of regional distribution as providing the opportunity for fast charging for

long-haul trucks reduces the necessary battery capacities and hence the costs of purchasing.

On the other hand, long-distance use increases the profitability of the battery electric truck

compared to the conventional truck. In such applications, the savings in operating costs can

pay off an electric truck's higher initial purchase cost in just a few years. Once the payback

period is reached, the operational benefits will continue to accrue, and the battery electric truck

will have an economical advantage over a conventional truck [Göckeler et al., 2020].

At present, payback estimations cannot easily be made for battery electric heavy-duty trucks

as only a few heavy electric trucks have been in service for long enough to provide actual

operation and maintenance data. However, various studies have estimated payback periods

for battery electric heavy-duty trucks for a variety of applications. The currently estimated TCO

for battery electric heavy-duty trucks is higher than that of their conventional counterparts,

despite significant savings in operating costs. This is due to the significantly higher acquisition

costs, which are currently at least twice those of diesel trucks [Mareev, 2018]. However, the

cost difference is expected to diminish over the coming decades. The findings from several

studies focusing on heavy-duty trucks in Germany suggest that battery electric trucks may

become cost-competitive with diesel trucks between 2025 and 2030, both in distribution and

in long-haul applications. These forecasts are primarily based on assumptions concerning

lower battery costs and higher production numbers [Mareev, 2018] [Basma et al., 2021].

Ultimately, the cost-competitiveness of battery electric heavy-duty trucks will largely depend

on the development of certain factors in the coming decades, some of which are subject to

very high levels of uncertainty. The most important of these factors is the development of

production volume, battery prices, and energy prices, as well as the development of the

residual value of electric trucks. Battery cost developments are highly dependent on

technological developments and economies of scale. Vehicle battery prices in general are

currently witnessing a dynamic development that has paid off in lower vehicle costs in the

passenger car and light-duty vehicle sectors, and this is also expected to extend to the heavy-

duty truck segment soon. The faster the evolution of battery prices, the sooner the battery

electric heavy-duty trucks will become more profitable [Burke & Sinha, 2020].

Energy costs are also a component of the cost that is subject to future uncertainty. While there

are already some estimates of the evolution of diesel and electricity prices over the next

decade, they involve a very high level of uncertainty. Nevertheless, in most of the scenarios

examined in the literature, future expectations of electricity and diesel fuel prices enhance the

TCO parity for a battery-powered truck with that of its diesel-powered counterpart in the near

future.

In general, studies reviewed in this work, such as [Unterlohner, 2021], [Mareev, 2018] and

[Burke & Sinha, 2020], indicate that battery electric technology represents the most cost-

effective option in the long run compared to all other zero-emission technologies for heavy-

duty trucks.

1.2.2.3 Ecological Impact

The emissions caused by a vehicle are not only limited to the direct emissions that arise when

the vehicle is used but also extend to the indirect emissions that result from the manufacture

and recycling of the respective vehicle and the associated infrastructure as well as from the

production of fuel or electricity used by the vehicle. The issue of sustainability has recently

become very controversial for electric vehicles in particular. On the one hand, electric mobility

is seen as a key option for reducing emissions and achieving national climate targets in the

transport sector since electric vehicles have a decisive advantage over combustion engines

because they do not emit any local emissions and are therefore particularly suitable for use in

cities and densely populated areas, where emissions limits are often exceeded. On the other

hand, electric vehicles present a unique challenge as they use electricity grids to charge their

batteries and also cause emissions when electricity is generated from various power plants. In

addition, a variety of materials that have adverse environmental impacts are used in the

manufacture of battery cells. Therefore, the origin of the raw materials that are used and the

non-recyclability are among the critical points that make the sustainability of electric vehicles

questionable.

To assess the environmental impact of electric vehicles and compare them to conventional

vehicles, a full life cycle analysis (LCA) is usually performed for the vehicle in question and its

associated infrastructure. The LCA accounts for emissions associated with all phases of a

vehicle over its life cycle. This includes the manufacturing and operation phases as well as the

disposal phase at the end of the vehicle's service life [Nordelöf et al., 2014]. In general, the

analysis of the exact environmental impacts of battery electric vehicles is still a topic for

research and development. In the automotive sector, the results of several studies show that

the LCA of electric vehicles is much better than that of fossil-fueled vehicles, even in relatively

dirty power grids [Helmers et al., 2017] [Nordelöf et al., 2014]. However, although these

analyses can be complex and incomplete for heavy-duty trucks due to the lack of sufficient

data, some studies analyzed the environmental impact of battery electric heavy-duty trucks

and compared it with their diesel truck counterparts. On this basis, the environmental impact

of heavy battery electric trucks will be discussed below, according to the life phases of a truck.

Manufacturing phase

When analyzing emissions at the manufacturing stage of a vehicle, emissions associated with

the manufacture of all individual vehicle components are considered, including the engine or

motor, transmission, body, and batteries. Depending on the truck technology, the fabrication

of related infrastructure is also considered at this stage. The vehicle chassis generally accounts

for the largest share of total manufacturing GHG emissions. Since the same chassis is usually

used to build all vehicles under comparison regardless of their powertrain technology, when

excluding the battery, differences in vehicle powertrains to be compared are usually so small

that they are often neglected in the LCA. However, significantly high emissions originate from

battery production and, as a result, manufacturing phase emissions represent a large part of

the total vehicle life cycle emissions for a battery electric vehicle. Although the results of recent

studies that have investigated the effect of battery production on the total emissions of an

electric vehicle vary widely, some studies indicate that actual battery production accounts for

30–50% of GHG emissions [Wietschel et al., 2019a]. However, the GHG emissions that result

from the manufacture of vehicle batteries depend on several factors, and, as many studies

show, the largest share of emissions comes from the electricity used in the production process.

Using cleaner electricity can thus significantly reduce the emissions attributable to battery

manufacturing whereby factory efficiency also plays a role in this context as larger and more

efficient factories usually consume less electricity and therefore have lower emissions per kWh

of the batteries produced [Wietschel et al., 2019a]. The type of battery chemistry and the

materials used in manufacturing also make a difference, with some chemistry mixes containing

higher concentrations of energy-dense metals [Hall & Lutsey, 2018]. Since GHG emissions

are calculated per battery capacity, battery size is a relevant factor that influences the GHG

balance in electric vehicles. According to [Romare & Dahllöf, 2017] the GHG emissions are

relatively linear in relation to the battery size and thus, in general, battery production has more

emissions in heavy-duty trucks than in other vehicles since they also require larger battery

sizes. In addition, battery chemistry may also play a role as the use of certain materials in the

truck batteries may results in more emissions.

From these deliberations, it is evident that a battery electric truck has a larger environmental

burden in the manufacturing phase than a conventional truck. However, a significant

improvement is likely expected in the short term as a result of increased efficiency and the use

of low-carbon electricity in battery production.

Operation phase

This phase is generally characterized by emissions associated with fuel or electricity used to

operate the vehicle as well as emissions associated with vehicle maintenance. Among others,

this includes emissions associated with the production and transportation of fuel or electricity

as well as its end use.

A battery electric vehicle does not emit any pollutants while driving, as there is no onboard

source for emitting pollutants and, for this reason, it is also referred to as a zero-emission

vehicle. However, this is only true when viewed locally, i.e. in terms of the TTW emissions as

the generation of electricity used to charge a vehicle battery produces pollutant emissions, i.e.

WTT emissions. For diesel trucks, the largest percentage of emissions comes from the use of

fuel in the engine. In addition, diesel production also contributes a given percentage of

emissions and thus the complete upstream chains, i.e. WTW emissions, must be considered

in the analysis.

For electric trucks, the amount of GHG emissions associated with the operation phase will

depend on the emission characteristics of the power plants that provide the required electricity.

However, as a result of the higher efficiency of electric motors and the capability of generating

electricity from low-carbon sources, electric vehicles usually have lower emissions in the

operation phase compared to similar internal combustion engine vehicles. This also applies to

the heavy-duty truck category. Some studies have already shown that battery electric heavy-

duty trucks generally have lower operating emissions than those of conventional trucks as well

as trucks of other technologies such as fuel cell, e-fuel, or compressed natural gas trucks,

especially due to the increase in the share of renewable energies in the provision of electricity

[Earl et al., 2018] [Moultak et al., 2017]. There is also a great potential to reduce emissions

from operating a heavy battery electric truck to zero if it is only recharged using electricity from

renewable sources such as solar or wind energy, which would be essential to achieve long-

term climate goals.

For emissions associated with vehicle maintenance, there is not enough data to balance the

maintenance of a battery-powered electric truck. However, electric truck maintenance certainly

tends to be less energy-intensive and thus causes fewer pollutant emissions.

End-of-life phase

At the end of the vehicle's service life, emissions associated with the disposal or recycling of

the vehicle are also considered. In general, the disposal or recycling of a battery electric vehicle

would be similar to that of a conventional vehicle, except for batteries. At the end of their normal

service life, spent vehicle batteries may have a second life in other applications such as

stationary storage in the electricity grid, which can significantly improve the balance of battery

electric vehicle emissions [Hall & Lutsey, 2018]. However, the battery will ultimately eventually

be recycled.

The question concerning the impact of the carbon footprint of battery recycling on the total

ecological balance of battery electric vehicles has been raised in some literature and is also

still the subject of current research. Recycling the metals contained in the battery – the most

important material for recycling – is considered to have significant environmental benefits as

the recycling process can reduce the environmental impacts of the battery production stage by

using recycled materials instead of raw materials in the production of new batteries [Aichberger

& Jungmeier, 2020]. Generally, thanks to the development of battery recycling processes and

increased energy efficiency, the impact of battery disposal on the overall vehicle life cycle

emissions will be minimal [Helms et al., 2016].

Overall environmental impact

Although the battery electric truck has a larger environmental impact than conventional diesel

trucks in both the production and end-of-life phases because of its battery, it can gradually

compensate for this difference in the use phase by lower emissions during operation. Current

studies show that when considering the full life cycle analysis, battery electric heavy-duty

trucks actually outperform their diesel-powered counterparts and all other types of zero-

emission heavy-duty trucks, including fuel cell trucks, in terms of their environmental impact

and primary energy consumption, even when using the current electricity mix. This is mainly

due to the high efficiency of the powertrain of battery electric vehicles [Sen et al., 2017]

[Mareev, 2018] [Thinkstep & Prognos, 2017].

However, as in the case of the assessment of economic feasibility, the results of the

assessment of emissions also depend on several factors and hence the results should always

be evaluated in light of the assumptions made.

Different usage profiles for truck users lead to dramatically different results. For example, in

the case of long-haul trucks, the emissions from operation dominate to a large extent, so that

the emissions in the vehicle manufacture and disposal phases only play a minor role in the

total LCA. Additionally, the longer the battery service life, the greater the advantages of a

battery electric truck.

Ultimately, the overall balance of battery electric trucks is closely related to the energy mix of

electricity generation as emissions are significantly reduced if electricity is used that is

exclusively derived from renewable energy sources such as wind and solar energy. On the

other hand, additional emission reductions can be achieved to further improve the climate

balance of battery electric vehicles by increasing the overall energy efficiency of the vehicles

as well as by improving the carbon footprint of battery production and recycling [Wietschel et

al., 2019b] [Helms et al., 2016].

The infrastructure's share of the environmental impacts is also considered very low because

infrastructure usually has a long service life and is often used by several trucks.

1.2.2.4 Noise Emissions

The operation of heavy-duty trucks is associated with significant noise emissions which put a

strain on both workers and residents, especially in urban environments. Accordingly, reducing

noise emissions is also an important factor in the further development of these trucks.

However, the sources of noise associated with truck operations vary widely. While driving the

truck there is noise caused by the use of the engine and noise resulting from the wheels rolling

on the road. In conventional heavy trucks, engine noise usually dominates tire noise up to 50

km/h [Raiber et al., 2014]. Moreover, additional noise may be caused when the truck is

stationary by operating the engine at a standstill.

Electric vehicles are generally considered to be quieter compared to conventional vehicles.

Although no actual studies on noise emission of battery electric heavy-duty trucks are

available, low noise from electric trucks is reported in some literature as well as by certain

manufacturers. For example, Volvo advertises that its electric trucks, designed for local

deliveries, are less noisy compared to their diesel counterparts [Volvo, 2022]. Based on

predictions in some literature, the electrification of heavy-duty trucks can significantly reduce

noise emissions compared to conventional diesel trucks, particularly in urban areas because

an electric motor produces less noise than an internal combustion engine. This gives electric

trucks an advantage when driving at speeds under 50 km/h. In addition, electric trucks do not

generate any motor noise when they are stationary. In some cities, there are restrictions on

delivery times for trucks due to noise in certain areas such as residential and other noise-

sensitive areas and such restrictions could potentially be lifted for the quieter electric vehicles

in the future. Deliveries during the night and early morning hours can reduce congestion during

the day, which in turn will often result in lower delivery costs [Karle, 2017] [Gräbener, 2017]

[Raiber et al., 2014].

1.2.2.5 Further Aspects

Although electrification technologies have recently spread rapidly in the passenger cars and

light vehicles sectors, the classic internal combustion engines are still dominant in the heavy-

duty truck sector today due to the good energy storage capacity of the fuel that other

technologies still cannot surpass [ACEA, 2021] [Basma & Rodríguez, 2021].

Apart from the high investment costs, some other factors currently limit the use of battery

electric heavy-duty trucks whereby one of the biggest challenges for the widespread

implementation of this truck technology is the limited capacity of the battery and lower energy

density compared to conventional fuels. In addition, a portion of the battery's weight is not

useful, as 20% of the battery's capacity is generally left unused to maintain the life cycle.

Hence, for heavy-duty trucks, especially long-haul trucks, due to the high-power requirements

and long operation times, heavy and excessively expensive batteries will be required. This is

also undesirable since a large battery occupies a certain percentage of the vehicle's volume

and leads to an increase in the vehicle's weight, which contributes to increased driving power

requirements. In addition, the payload capacity could be compromised by the added battery

weight. However, vehicle battery technologies are subject to rapid development, as increasing

efforts and resources are constantly being put into their development whereby current

developments mainly focus on reducing the cost of production and increasing the storage

density by enhancing the efficiency of packing cells into smaller and lighter units [Thinkstep &

Prognos, 2017] [ Moultak et al., 2017] [Lienkamp, 2014].

Also, at present only relatively limited ranges can be achieved with battery electric heavy-duty

trucks compared to diesel trucks, due to the battery's weight and high costs [Lienkamp &

Homm, 2018]. However, the ranges of battery electric heavy-duty trucks currently available on

the market are sufficient to cover a large portion of the daily distances required in most

application, especially since electric motors are more efficient than conventional engines, and

hence batteries will not need the same energy density as diesel. For instance, a battery with

an energy density that is three times lower than that of diesel may be sufficient to achieve the

same driving range as the diesel equivalent [Earl et al., 2018] [IEA, 2019] [Den Boer et al.,

2013].

Access to charging points is another obstacle to electric mobility as the public charging

infrastructure is not sufficiently developed to date. On the other hand, the use of private

charging stations in warehouses also entails additional costs [IEA, 2021] [Hildermeier & Jahn,

2020].

However, these challenges are circumvented for trucks with special uses that are used in urban

and local application transport such as municipal service trucks, construction site trucks, and

airport trucks, as these applications involve relatively short daily trips and many start-stop

phases, potentiality with recuperation, wherefore smaller electric battery packs are required

[Moultak et al., 2017] [Smith et al., 2020]. It is also often possible to install charging stations at

work sites for these applications.

In general, the interest in battery electric trucks is gradually increasing and significant progress

has already been made in all aspects recently in parallel with an increase in the demand for

battery electric trucks and a larger number of models available on the market. Battery electric

trucks are expected to replace traditional diesel trucks in the coming decades, since many of

the single models available in the market today are expected to go into series production in

larger numbers. In addition, according to a statement by the European Association of

Automobile Manufacturers (Association des Constructeurs Européens d'Automobiles ACEA)

in December 2020, all new truck sales will be fossil-free by 2040. In parallel, large projects are

being undertaken globally to provide a suitable infrastructure for battery electric heavy-duty

trucks in all applications [IEA, 2021] [Basma & Rodríguez, 2021].

1.2.3 Calculation of Energy Consumption in Heavy-Duty Truck Applications

The comparison between alternative vehicles is usually based on determining the vehicle

energy demand, on the basis of which the emissions and economics of the vehicle in question

are then determined. The energy consumption of a vehicle for a given application depends on

the efficiency of its components, particularly the engine, whose efficiency usually varies at

different operating points depending on the speed and load applied. Therefore, there are

significant differences in determining the efficiency and consumption of trucks with different

applications. The consumption data provided by the manufacturers allows for an initial

comparison but does not reveal the real consumption associated with a particular application.

Simulation-based methods are particularly suitable for this purpose and are the easiest and

least expensive to implement. The main advantage of using a vehicle simulation model is its

reproducibility, that ensure the repetition of the same result with each new simulation with the

same initial data, which allows the evaluation of modifications or variable parameters more

easily, as well as the possibility of direct comparison and analysis of alternative vehicle

powertrain technologies in different application scenarios under various operating conditions.

This is helpful for making investment decisions on the one hand, and on the other hand, it

allows the identification of usage parameters that have an appropriate effect on energy

consumption, thereby allowing for the possibility of modification and improvement [Liebl et al.,

2014] [Haupt, 2013].

The vehicle model represents all the essential components that are relevant for the

consumption, however, when only energy demand is required to be determined, detailed

modeling of the relevant system components is usually not necessary, as the vehicle is

simulated based on the principle of cause and effect based on mathematical equations that

only describe the relationship between input and output variables without addressing the

internal mechanism of action. The required drive energy of the vehicle to be simulated is

determined by calculating the withdrawn energy from the system by driving resistance, which

is considered the same for all comparison vehicles, and losses along the drive train. For

vehicles with electric drives, the energy that is restored through recuperative braking is also

determined and supplied with energy again [Liebl et al., 2014]. This can be represented by

either forward simulation or backward simulation [Gräbener, 2017]. In the forward simulation,

the computation path corresponds to the causal chain and energy flows of the real vehicle,

where driving is simulated through a driver model in addition to the vehicle model in a closed

control loop. The driver model represents the controller that gives signals in order to regulate

the vehicle speed to comply with the pre-set driving cycle. This method is useful when there is

another purpose of the simulation in addition to determining the energy consumption, for

example, to optimize the regulation strategies to reduce the consumption in the vehicle.

While in the backward simulation, the calculations are done in the opposite direction to the

energy flows in the real vehicle, where the driving resistance affecting the vehicle is calculated

based on the specific driving cycle and thus the required engine power is determined in the

vehicle model, without the need to a driver model. In comparison to the forward simulations,

the backward simulations are simpler and require less computational effort.

The simulation model is simplified or detailed depending on the availability of the necessary

parameters and input data and the level of detail required. In some works, main vehicle

components are modeled in more detail instead of relying only on their efficiency

characteristics, which is often intended for other purposes in addition to calculating the energy

requirements of the vehicle. For example, in [Mareev, 2018], the simulation model determines

the aging of the battery and thus the life span based on the vehicle energy requirements. While

in other works, like [Sripad & Viswanathan, 2017] and [Link et al., 2021], the energy required

to overcome the driving resistances is calculated with a single simplified equation using an

overall vehicle efficiency, which represents the efficiency from the battery to the wheel.

However, the final energy consumption of a vehicle depends not only on the vehicle

parameters but also on the operating conditions. Therefore, in order to determine the energy

consumption of heavy-duty trucks of different drive train technologies, a specific operating

profile that adequately describes the user's application should also be defined. A

representative operating profile is typically represented by exemplary driving cycles of the daily

routes of the respective truck and related operating data such as payload and operating times.

Driving cycle data is ideally presented in time-based speed profiles corresponding to each

individual trip and used as input data in the energy consumption simulation model. However,

some works in the literature instead simulate the relevant roads using the GPS coordinates of

the particular road and possibly supplemented with road elevation information. Subsequently,

constant speed values are assumed based on the maximum speed allowed on the given road,

as in [Mareev, 2018], [Earl et al., 2018] and [Zhao et al., 2018], or cycle-specific average speed

values based on driving cycle databases are used, as in [Sripad & Viswanathan, 2017] and

[Link, et al., 2021]. Similarly, constant acceleration and deceleration values are also assumed,

representing the vehicle's acceleration or braking to keep with the set speed value. NREL

DriveCAT database [NREL, 2021] provides data of different real driving cycles, including

highway and urban driving, for heavy-duty vehicles.

However, assuming constant speeds may be feasible in long-haul applications, where the truck

travels at near constant speed on highways most of the time. But in urban applications or local

applications, as is the case in this work, this cannot represent the realistic consumption of the

truck, since here the truck goes through frequent phases of stopping and accelerating, which

is significantly reflected in the consumption of the truck, especially for electric trucks, where

there is the possibility of recovering braking energy. Therefore, the calculations in this work

are based on representative operating cycles that include real driving cycles for the typical

daily trips of the studied catering lift truck.

In the literature, simulation-based energy demand approaches have been used to evaluate the

deployment of battery-powered heavy-duty trucks versus their conventional diesel-powered

counterparts, mainly based on cost and environmental balance. However, most of the studies

focus on long-distance applications such as [Mareev, 2018] and [Burke & Sinha, 2020], while

very few of them cover urban and regional distribution applications such as [Link et al., 2021].

While the replacement of conventional trucks with battery-powered trucks has not been studied

for either local applications or special-purpose truck applications. The approach presented in

this work distinct from other works in that it deals with a specific, previously unaddressed

heavy-duty truck application, where the energy consumption of the catering lift truck is

determined using a simulation model based on real operational data, including recorded

dynamic speed data, as experienced by the user in daily operation of the truck.

On the other hand, when considering auxiliaries in calculating the energy requirements of

trucks, the focus in the literature is mostly on certain units such as heating and cooling in the

driver's cab, cooling in the freight container and driver assistance functions, where constant

consumption is usually assumed [Jerratsch, 2021]. However, these auxiliaries are considered

irrelevant in this work, since the truck is generally only driven for short periods of time, and the

container cooling is mostly not used in the truck under consideration because it mainly

transports hot food to the airplane and waste on the return. In contrast, in this work, the energy

demand for a different type of secondary system with special purposes, namely the hydraulic

container lifting system in the catering lift truck, is modeled and analyzed.

1.3 Research Questions and Objectives

Although there has been recent research on the replacement of traditional heavy-duty trucks

with battery-powered electric trucks, most studies focus on long-distance and some urban

applications. However, the findings from these studies cannot be directly applied to heavy-duty

trucks in local applications. The operating profiles of these trucks differ, especially when

considering trucks with additional systems for specialized uses beyond transportation. In such

cases, the trucks often operate at low speeds, make frequent stops with acceleration and

deceleration phases, and exhibit distinct energy consumption characteristics and associated

emissions. Additionally, these operating conditions present more opportunities for regenerative

braking energy. Unfortunately, these aspects have not been adequately addressed in

comparative analyses within the existing literature. Furthermore, the impact of power train

electrification on the energy consumption of specialized ancillary systems in heavy-duty trucks

has not been thoroughly explored. In addition, heavy-duty trucks are frequently used in close

proximity to people in local settings, necessitating a discussion not only on GHG emissions but

also on noise emissions generated by these trucks.

Considering the current state of knowledge, the following research questions arise, which this

study aims to address:

• What are the energy consumption characteristics of specialized heavy-duty trucks in

local applications?

• What is the economic feasibility of sustainable usage of battery electric trucks in

specific local applications, considering the high cost pressure for investments and the

lower savings associated with these applications?

• How does the real-world usage profile, as experienced by users in their daily lives,

influence truck energy consumption and the competitiveness of battery electric trucks?

• How does the electrification of the power train impact the energy consumption of

specialized ancillary systems in heavy-duty trucks?

• What are the expected reductions in GHG emissions and noise emissions resulting

from the utilization of electric drive systems in heavy-duty trucks for these applications?

In light of these considerations, this study analyzes and evaluates the competitiveness of an

electric prototype truck developed within the research project “eLift” (part of the E-PORT AN

project [E-PORT AN, 2015] [NOW, 2013]) compared to a conventional catering lift truck. The

analysis focuses on energy consumption, costs, and environmental impact over the estimated

service life of the truck, taking into account realistic individual usage profiles. These trucks are

employed at various airports worldwide for delivering catering and other supplies to airplanes.

However, this study specifically examines the catering lift truck used at Frankfurt Airport as a

case study.

The primary goals of this study, derived from the research questions, are as follows:

• Establish authentic operational profiles for the examined truck, considering the

pertinent contextual factors.

• Analyze the energy consumption patterns of the truck and delineate the distribution

between the driving system and the lifting system.

• Assess the effectiveness of electrification in enhancing the efficiency of the driving

system and the lifting system independently, as well as the overall vehicle efficiency.

• Showcase the benefits of recuperation in reducing energy consumption and increasing

energy efficiency.

• Evaluate the economic competitiveness of the electric truck compared to the

conventional diesel truck under various potential scenarios.

• Examine the potential environmental impacts of electrification in terms of GHG

emissions, primary energy demand (PED), and noise emissions in this particular

application.

1.4 Procedure of the Work

This thesis presents a comprehensive analysis approach to assess the competitiveness of an

electrified prototype truck compared to conventional catering lift trucks. The aim is to identify

potential advantages and disadvantages throughout the truck's service life. Figure 1-1

illustrates the basic steps of the approach.

Figure 1-1: Approach for evaluating the competitiveness of the electrified prototype truck

versus conventional catering lift trucks

Real Usage Profile and

Consumption

Evaluation Analysis

Analysis Parameters

Operating

Scenarios

Efficiency-

Analysis

TCO-Analysis

LCA-Analysis

Noise-Analysis

Energy-Consumption Simulation Model

Discussion of Potential Scenarios and Improvement

Possibilities

Input Data

Driving

Cycles

Lifting

Cycle

Noise

Measurement

Data Processing

Operating

Conditions

Vehicle

Parameters

Measurement Concept

The performance of both trucks is evaluated based on the actual usage profile of catering lift

trucks at Frankfurt Airport. This involves analyzing the operating conditions, determining

relevant work tasks and framework conditions, and recording speed profiles for the real driving

cycles of the truck, as well as hydraulic measurement data for the complete duty cycle of the

lifting system.

Using the collected operating information and measurement data, daily operating scenarios for

the truck are defined. A detailed simulation model is then employed to determine the realistic

daily energy consumption for both catering lift trucks considered in this work.

The first aspect of the evaluation focuses on analyzing and comparing the energy efficiency of

the trucks to demonstrate the efficiency gains achieved through electrification.

Considering the trade-off between increasing efficiency and ensuring profitability, the second

aspect of the evaluation involves analyzing the life cycle cost of an electric catering lift truck

and comparing it to its conventional counterpart. This analysis is based on the calculated

energy consumption results for the defined operating scenarios, taking into account all relevant

information about the truck's future operation under various possible scenarios.

Additionally, the evaluation analysis pays particular attention to assessing the environmental

impacts of the trucks. The third aspect of the evaluation examines the potential reduction in

GHG emissions and PED associated with the calculated energy consumption for the defined

operating scenarios. Furthermore, the analysis explores the potential reduction in noise

emissions based on dedicated noise measurements conducted specifically for this purpose.

The presented results aim to provide a valuable contribution to the ongoing transition in fleets

of heavy-duty trucks with local applications, as part of efforts to achieve the climate goals.

Based on the analyses conducted in this study, conclusions can be drawn regarding the

relationship between the truck's energy consumption, its economy, and the respective usage

profiles. This offers valuable insights for policymakers, corporate users, and fleet operators

operating in similar application areas. Moreover, the results offer an overview that facilitates

the identification of key factors influencing a vehicle's energy consumption, enabling the

assessment of the impact of usage modifications aimed at improving efficiency or reducing

costs.

1.5 Outline of the Work

This dissertation is divided into 9 chapters, which are briefly described below.

1. The first chapter introduces the work. In addition to the current state to relevant aspects

concerning battery electric heavy-duty trucks, the need for research and the objective of the

work are described. Then the procedure taken in this work to evaluate the feasibility of the

catering lift trucks under consideration is explained.

2. The second chapter provides an analysis of the usage profiles and framework conditions.

For this purpose, the trucks in question for the application and their specific purpose are

described first. The typical usage profiles and relevant framework conditions are then

described based on the operating data of catering lift trucks at Frankfurt Airport. This includes

a description of the basic workflow, typical work tasks, and operating times of the truck. In

order to obtain accurate operating data, a data logger is used to record the real driving profiles

of the trucks and representative work cycles are defined accordingly.

3. The third chapter describes the mathematical configuration of the numerical simulation

model developed to calculate the energy requirements for both driving and lifting. For this

purpose, the components of the catering lift truck are modeled that are involved in the

transmission of power for all related work tasks. The simulation model is applied to the

prototype truck as well as a conventional diesel truck as a reference for comparison. Finally,

the operating profiles described in the previous chapter are processed to determine the realistic

daily energy and fuel consumption using the available input data of the vehicles in question.

4. In the fourth chapter, the results of the simulation models are used to compare the efficiency

of the vehicles being investigated. The performance of the two catering lift trucks is evaluated

for the same work tasks under the same operating conditions and all related work functions

are analyzed with respect to energy consumption, work done, and losses generated. With the

help of simulation results, the possibility of energy saving during operation through

electrification is demonstrated. The amount of energy that can be saved in the electric truck by

braking energy recovery is also demonstrated.

5. In the fifth chapter, a TCO analysis of the respective trucks is performed. For this purpose,

operating scenarios are determined based on the previously described work cycles.

Furthermore, the development of cost parameters relevant to future operation is discussed.

The simulation results are used to calculate fuel and energy consumption costs. Accordingly,

all costs are recorded over the entire life cycle, and subsequently analyzed for both the

electrified and the reference catering lift trucks. Finally, cash values of the life cycle costs are

determined to compare and evaluate the economic feasibility of the alternative truck for the

determined scenarios. Sensitivity analyses are conducted to be able to examine the effect of

variable parameters.

6. In the sixth chapter, the impact of the GHGs and the PED over the life cycle of a catering lift

truck are analyzed and processes that represent differences between the two vehicles under

consideration are taken into account. This includes the manufacturing, operating, and end-of-

life phases. For this purpose, the definition of the analysis framework and the criteria included

in the analysis are first discussed and the emissions associated with the provision and

transportation of the fuel or electricity needed to operate the vehicles as well as the

manufacture and recycling of batteries are specified. In addition, the production of electricity

from 100% renewable energies is taken into account.

Consequently, the environmental impacts of the vehicles in question are determined whereby

the simulation results are used for the estimated diesel and electricity consumption. The trucks

are then compared according to the same operating scenarios that were defined for the TCO

analysis in Chapter 5. Finally, the influence of important factors is discussed in the sensitivity

analysis.

7. In the seventh chapter, an assessment of the noise emissions of the catering lift trucks under

consideration is made. For this purpose, acoustic standards and measurement methods are

first discussed. In addition, the measuring environment and the utilized measuring equipment

are presented. After that, the concept of acoustic measurement, which was developed and

implemented for noise measurement for the catering lift truck application, is explained. Finally,

both types of trucks are examined and compared for noise emissions when driving as well as

when operating the lifting system.

8. In the eighth chapter, improvement possibilities to increase energy efficiency of the lifting

system of the electric truck are discussed. The measures where there is the greatest energy-

saving potential are implemented in the simulation model and the results are compared with

those of the current system to determine the increase in energy efficiency.

9. The work closes with a summary and discussion of the results obtained for the examined

battery electric and the reference diesel catering lift trucks according to the defined operating

scenarios regarding their energy consumption and efficiencies, the TCO, the environmental

impact and noise emission. In addition, an outlook is provided for the connection of further

scientific work and the application in practice.

2 Reference Trucks and Usage Profile

In order to evaluate the battery electric prototype truck in comparison to the conventional diesel

truck, it is first necessary to describe the two vehicles that will be compared and to analyze

and define the work tasks and typical functions of such vehicles whereby the operating

conditions and important factors affecting the performance requirements are also presented.

Thereupon, representative work cycles are defined to be utilized next to determine the resulting

energy consumption using modeling and simulation. The simulation results are used later in

the implementation of the analyses that provide an objective comparison between the different

drive technologies to investigate the competitiveness of the electric catering lift truck compared

to the diesel truck.

2.1 Catering Lift Trucks

Catering lift trucks are specialized vehicles that are operated as a fleet at almost every airport

and are of tremendous importance when it comes to the speedy loading of airplanes. The

operators of such special-purpose vehicles at German airports are mainly catering companies.

The task of a catering lift truck is to provide the airplane on the ground with food and beverages,

as well as all other possible items needed on the plane such as newspapers and magazines,

blankets, pillows, headphones, toiletries, etc. [Klußmann & Malik, 2012]. One key challenge

that a catering truck has to overcome is the different heights of the airplanes it services that

need to be covered. As a result, catering lift trucks are available in different types and sizes

depending on the height the vehicle must cover and the permitted payloads. Hence, the choice

of vehicle depends on the type of airplane to be served [DOLL, 2022].

2.1.1 Reference Diesel Truck

A catering lift truck from DOLL Fahrzeugbau GmbH in size M, shown in Figure 2-1, is

considered a reference for this work. The truck is currently used at Frankfurt Airport by the

operator company LSG Sky Chefs. The detailed technical specifications of the diesel truck as

well as the electrified prototype truck are listed in Appendix A of this work. The vehicle has a

total permissible weight of 21 tons and a payload of 4.5 tons. The construction of this vehicle

consists of a commercial two-axle truck chassis, a driver's cab, and a lifting system. The vehicle

is driven by a six-cylinder diesel engine which achieves 142 kW at 2,000 rpm and 809 Nm at

1,500 rpm and meets the Euro 4 emission standard, as this emission class was still acceptable

at the time the truck was purchased and is not considered so low as it is today. The mechanical

energy is transmitted from the diesel engine to the rear wheels via a clutch, an automatic

transmission with five forward gears and one reverse gear, and a differential.

The lifting system consists of a subframe with a support system, single-stroke scissors, a

hydraulic system, and a catering container. The lifting system has the task of lifting the catering

container in the vertical direction to the required height. After parking next to the airplane, the

support legs extend to stabilize the vehicle after which the catering container rises with the

help of a double scissor lift until it is level with the height of the airplane door. For the loading

and unloading process, the catering container is equipped with a front platform, as shown in

Figure 2-1, that can be moved laterally to adjust the height of the container to align with that of

the door of the airplane. The front platform is also equipped with an extendible part, namely

the front platform tongue, which can be extended to dock the front platform onto the airplane

door, thereby creating a bridge to the inside of the airplane.

Figure 2-1: Reference diesel catering lift truck (Source: DOLL Fahrzeugbau GmbH)

All functions of the lifting system are driven hydraulically. The hydraulic power is provided to

the lifting system by an axial piston pump with constant displacement driven by the diesel

engine. The pump supplies the hydraulic circuit with oil from the tank. The distribution of the

oil flow to the individual cylinder actuators is adjusted to the respective operating function via

electrically controlled valves. An inlet flow regulator valve associated with every valve controls

a limited flow to prevent higher flow feed. The cylinder actuator converts hydraulic energy back

into mechanical energy and thus the respective function is performed. Since the functions of

the lifting system are operated separately, each function has its own hydraulic circuit that

transmits the hydraulic energy. For the main function of lifting/lowering the catering container,

two double-stage telescopic cylinders are arranged on the scissor lift with a stroke of 1,412

mm. This allows the catering container to be raised to 4.8 m from the chassis with a speed of

approximately 0.06 m/s. To ensure the lowering process even in emergency cases, a drain

valve is additionally arranged in front of the telescopic cylinder which can be operated

manually. This valve ensures a maximum lowering speed of 0.15 m/s in an emergency. For

each of the other functions, namely extending the support legs, shifting the front platform

laterally, and extending the front platform tongue, a double-acting cylinder is arranged. The

front platform can be displaced by a maximum of 1.46 m and the front platform tongue can be

Front platform

extended by a maximum of 0.6 m. The support legs extend until they rest on the ground, which

is a maximum distance of 0.38 m. Since the axial piston pump can always deliver a constant

oil volume, a load-sensing valve is used in the vicinity of the pump, so that the system pressure

is regulated depending on the function being operated.

The catering lift truck can not only be used on the airport apron but also on public roads.

However, during road travel, the lifting system cannot be operated, and the front platform

remains folded beside the driver's cab. The lifting system is thus only operated while

processing an airplane when the vehicle is stationary. While driving on the airport apron, the

average driving speed remains relatively low, and a constant driving speed is generally not

maintained. This type of driving profile fits perfectly into the application profile of electric

vehicles.

2.1.2 Electrified Prototype Truck

The battery electric prototype of the catering lift truck is shown in Figure 2-2. The basic

structure of the truck is almost identical to that of the conventional truck. In the developed

prototype vehicle, the classic diesel engine is replaced by two asynchronous motors, which

together have a maximum operating power of 138 kW and a maximum speed of 2,500 rpm.

The power supply is realized by four LiFePO4 batteries and the entire unit of the four batteries

has a capacity of 113 kWh. The power is transmitted to the motors via the power electronics.

In the same way as in the conventional vehicle, the energy of the motor is transferred

mechanically to the rear wheels using the same clutch, five-speed automatic transmission, and

differential.

Figure 2-2: Electrified catering lift truck (Source: DOLL Fahrzeugbau GmbH)

The function of lifting the catering container and extending the support legs remains hydraulic

and hence the hydraulic lifting system is retained although it is driven electrically by the main

electric motor. However, the functions of the front platform and the front platform tongue are

separated from the hydraulic lifting system. Each function is equipped with its own electric

drive, which is represented by an asynchronous three-phase geared motor and a spindle to

convert the rotational movement of the motor into translational movement. This reduces the

size and challenges of the hydraulic system.

However, the batteries cause an increase in weight and required installation space of the

electrified drive compared to the current conventional drives and while the permissible total

weight of 21 tons is maintained, the payload is reduced by 734 kg. The prototype truck can be

used on the street and in the operational area of the airport without restrictions.

2.2 Operating Profile and Working Conditions

The description of a realistic operating profile represents the basis for the evaluation analysis

of vehicles and hence representative operating profiles should be defined that come as close

as possible to the real conditions of use. The main task of the catering lift truck is the delivery

of articles from the catering kitchens to the airplane and back. On the ground, the catering

goods to be transported are prepared in the kitchen and placed in trolleys. A typical working

task begins when the catering lift truck is parked on a so-called forward ramp next to the kitchen

to be loaded with the freshly packed trolleys. Usually, the truck has to be on-site about an hour

before driving to the airport as the truck loading time is approximately one hour. Since the

kitchen is located outside the airport apron, the truck is first driven on the normal road from the

kitchen to the airport site. Before entering the airport, the truck must be stopped briefly and

checked at the checkpoint. The truck is then driven on the airport apron to the airplane to be

served and parked next to it. For loading and unloading the airplane, the four support legs are

first extended to keep the vehicle stable while delivering the catering. Then, the container is

lifted with the help of a double scissor lift to the level of the airplane door. Depending on the

relative position between the truck and the airplane, the front platform is moved sideways to

adjust it to the airplane door. Once this step is completed, the front platform tongue is extended

to dock the front platform to the airplane door. The airplane is then supplied with catering and

cleared from catering waste at the same time. The airplane handling time, including the loading

and unloading process, is usually approximately one hour. When the loading and unloading

processes are completed, the front platform tongue is retracted again, the front platform is

moved back, the container is lowered, and the support legs are retracted. The catering lift truck

subsequently leaves the airport apron and drives back to the kitchen and parks on the return

ramp so that the trolleys with the used materials and leftover food can be unloaded. It takes

approximately one hour for the truck to be unloaded.

Although the working tasks of the catering lift truck are clearly defined, the working conditions

are different as they are dependent on many factors, especially since the total distance traveled

on every route varies according to the parking position of the airplane for which the delivery is

intended. The velocity profiles of variant routes also differ relative to the driver behavior and

airport traffic conditions while the weight of the payload varies depending on the number and

type of trolleys and the number of articles that need to be transported. In addition, the functions

of the lifting system are also subject to different working conditions and as environmental

conditions affect the speed of the hydraulic actuators, the time taken to complete the same

function may vary every time. Furthermore, the lateral displacement of the front platform and

the extension of the front platform tongue also depend on the relative position between the

catering container and the airplane door. That is, the front platform and the frontal platform

tongue do not have to extend completely every time but should only move until they reach the

airplane door. Consequently, in this work, the operating profile and working conditions are

simplified by taking the typical tasks of the catering lift truck into account to determine ideal

work cycles that serve the comparison purpose.

2.3 Representative Work Cycles and Measurement Datasets

Based on the analysis of the operating profile in the previous section, the typical work cycle for

a catering lift truck was determined which comprises representative movement and load

profiles that come as close as possible to the actual conditions of real work. The defined typical

work cycle is outlined in Figure 2-3.

Figure 2-3: Typical work cycle of a catering lift truck

Driving System

Lifting System

Kitchen

Airplane

Driving to

▪Extending the support

legs

▪Lifting the container

▪

Shifting the front platform

▪Extending the front

platform tongue

Driving back

▪Retracting the front

platform tongue

▪Shifting back the front

platform

▪Lowering the container

▪Retracting the support

legs

In real operation, it is not scheduled how often or how many operating hours every truck should

be used per day. However, based on the experience of LSG Sky Chefs, a catering lift truck is

used three times a day on average. Accordingly, three work cycles per day are assumed for

each catering lift truck in this analysis whereby one work cycle takes an average of 4 hours.

Thus, a truck is in use for an average of 12 hours a day. It is further assumed that a truck is in

use 7 days a week. Regarding the loading volume, a further assumption is made here, namely

that the vehicle is not fully loaded when in use.

As shown in Figure 2-3, a total of five functions are activated in every work cycle of a catering

lift truck, namely the functions of driving, support legs, catering container, front platform, and

front platform tongue. The overall system of the catering lift truck can be divided into two areas,

namely the driving system, which represents the mechanical traction powertrain, and the lifting

system, which represents the hydraulic setup with the scissor lift. The driving system is

responsible for the driving function, while all other functions are related to the lifting system.

As the two systems are operated separately, the lifting system cannot be operated while

driving, and hence, for a detailed investigation, the two systems are analyzed individually in

the following. To obtain realistic work cycles, measurement data were recorded under typical

conditions for the trucks considered and are presented in the following sections.

2.3.1 Driving Cycles

In this work, the driving power requirements of the catering lift truck are determined using the

simulation model based on real measurement data. To this end, a set of measurement driving

cycles were carried out at Frankfurt Airport with both the reference diesel catering lift truck and

the electric prototype truck. These cycles are intended to reflect the real use of the truck as

realistically as possible. Therefore, the driving cycles recorded with the catering lift truck under

consideration are presented below.

2.3.1.1 Definition of Representative Driving Routes

In its normal operation, the catering lift truck drives along two key sections. In the first section,

the truck drives from the kitchen to the checkpoint of the airport apron. The kitchen is the station

where the truck is loaded with catering material and is located in the area allocated to the LSG

Sky Chefs company. On its way, the truck drives on an ordinary city road with a maximum

speed of 50 km/h. The truck is then parked at the checkpoint and checked for a few minutes.

Since the route of this section is short and the outward journey is the same as the return

journey, the traveling velocity was measured only once for this section. The second section is

the route from the checkpoint to the airplane parking position. The maximum speed allowed

on the airport apron is 30 km/h and driving there is usually characterized by multiple

consecutive stops because of the airport traffic. Since there are various ways to get to the

individual parking positions of the airplanes, this means that different routes can be taken

depending on the location of the airplane and the time of the day. For the measurements, three

driving routes were defined comprising different routes to various airplane positions. Figure 2-

4 shows the mapping of the defined driving routes using Google Earth.

Figure 2-4: Google Earth mapping of the measurement driving cycles

Driving cycle 1

Driving cycle 2

Driving cycle 3

The chosen routes fulfill the two general objectives: first to cover the majority of operating

routes that span the work envelope of catering lift trucks, and second to obtain different driving

profiles that cover a series of modal events such as various levels of accelerations and

decelerations, constant speed as well as speed-fluctuation driving, and idling periods.

In the first driving cycle, the truck drives from the kitchen passing through the checkpoint to

Gate C14, where airplanes are most frequently parked, and then the same way back to the

checkpoint. In the second driving cycle, the truck drives from the checkpoint to gate A62 and

then returns to the checkpoint using the same route. This driving cycle is significantly longer

than the first one because gate A62 is further away than gate C14. In the third driving cycle,

the truck drives from the checkpoint to position 296, which is the most distant parking position

for airplanes. Finally, the truck drives through the checkpoint and returns to the kitchen. The

return journey takes place on the southern side of the airport and this cycle is the longest

because it goes around the entire airport apron. Thus, the first and third cycles involve a

combination of driving on the airport apron and normal city driving, while the second cycle does

not include city driving. As Figure 2-4 shows, in the first and second cycles, the return journey

is the same as the outward journey, which is not the case in the third cycle.

2.3.1.2 Measured Driving Cycles

Velocity measurements for the three routes shown in Figure 2-4 were taken once for the diesel

truck and once for the electric prototype truck with the same driver. However, although both

trucks drove the same routes, their velocity profiles show considerable differences in terms of

the time and distance traveled. These differences are sometimes due to the driving style and

at other times they result from the recording time because measurement data is lost if the

sensor is turned on too late or turned off too early. These differences in the driving cycles can

lead to significant variations in fuel consumption and thus in the energy efficiency of the vehicle.

Therefore, the driving cycles used for both truck simulation models must be identical to ensure

a reasonable comparison. Therefore, only the driving cycles with the electric truck are

considered for the simulation of the power demand while driving, since they have fewer speed

fluctuations and appear more realistic than those of the diesel truck.

The measurement data was recorded with the data logger VBOX i3, which was directly

attached to the vehicle. Among other data, location (GPS data), speed, and acceleration are

recorded with a recording rate of 100 Hz. For all valid measurements, the local time is

determined and linked to the associated measured values. The recorded data was processed

by filtering to smooth it and correct various measurement errors before being used as input

data for the simulation models. A detailed description of the processing steps is given in

Appendix B of this work. However, it should be noted that the driving cycles were carried out

without payload and the process of airplane catering. The weather was stable and sunny, and

the roads were dry.

The driving cycles recorded with the electric truck are shown in Figure 2-5, while their

characteristics are presented in Table 2-1. The driving cycles recorded with the diesel truck

are presented in the Appendix C of this work, however they are not used for the simulation

models.

Figure 2-5: Velocity profiles of the driving cycles of the electric catering lift truck

0 100 200 300 400 500 600

Velocity [km/h]

Time [s]

Driving cycle 1

0 200 400 600 800 1000 1200

Velocity [km/h]

Time [s]

Driving cycle 2

0 400 800 1200 1600 2000 2400

Velocity [km/h]

Time [s]

Driving cycle 3

Table 2-1: Characteristics of the driving cycles of the electric catering lift truck

Route

Distance

[km]

Duration

[s]

Max. speed

[km/h]

Average

speed

[km/h]

Stop time

[s]

Driving cycle 1

3.70

661

39.72

21.67

Driving cycle 2

7.78

1,295

38.68

22.41

Driving cycle 3

15.68

2,527

41.81

26.64

409

As can be observed in Figure 2-5, the driving cycles are dominated by start-stop phases and

low velocities, which can especially be seen in the first and second cycles. However, compared

to the cycles recorded with the diesel truck, the maximum speeds are reasonable and there

are no cases in which the speed limit is extremely exceeded in all cycles of the electric vehicle.

There is even a moderate proportion of idle time, and the average speeds are also reasonable

in all cycles.

2.3.1.3 Analysis of the Driving Cycles

For further investigation, the driving cycles are segmented into four driving modes based on

the instantaneous values of speed and acceleration and these are defined as idle, cruise,

acceleration, and braking. Table 2-2 demonstrates the definitions of the driving modes. The

vehicle is in acceleration mode when the acceleration is above 0.5 m/s2 and in braking mode

when the acceleration is below –0.5 m/s2, while for any acceleration value between these two

values, the vehicle is assumed to be in cruise mode. Idle mode represents the vehicle when it

is not moving, and this is set when the velocity value is zero.

Table 2-2: Definition of the driving modes

Driving mode

Conditions

Idle

𝑣=0

Cruise

𝑣>0 and −0.5>𝑎<0.5

Acceleration

𝑣>0 and 𝑎>0.5

Braking

𝑣>0 and 𝑎<−0.5

where 𝑣 is velocity in m/s and 𝑎 is acceleration in m/s2

Figure 2-6 shows the time ratio of driving modes in the driving cycles considered. As shown in

the figure, all driving cycles involve substantial proportions of accelerations and decelerations,

and the cruise mode only makes up an average of one-third of the overall driving time. Larger

braking proportions present a greater opportunity for the electric truck to benefit from the

possibility of regenerative braking. The percentage of idling varies from one driving cycle to

another, as this depends on the nature of the traffic. The third cycle is the longest one and has

the largest proportion of idling time. The different effects of these modes on consumption and

energy efficiency are further discussed in Chapter 3.1.

Figure 2-6: Time distribution of the driving cycles according to the driving modes

2.3.2 Lifting Cycles

A work cycle of the lifting system can be subdivided into two sections, each involving the

operation of the four functions of the lifting system. The first section represents the process of

lifting the catering container and aligning it with the airplane door. Consequently, it begins with

the extension of the four support legs, followed by the lifting of the catering container, the

moving of the front platform sideways, and finally the extension of the front platform tongue.

7.0%

29.7%

35.5%

27.8%

Driving cycle 1

Idle Acceleration Cruise Braking

3.5%

35.5%

26.6%

34.4%

Driving cycle 2

Idle Acceleration Cruise Braking

16.2%

22.3%

37.8%

23.7%

Driving cycle 3

Idle Acceleration Cruise Braking

11.2%

27.2%

34.2%

27.4%

Total driving cycles

Idle Acceleration Cruise Braking

During the process of loading and unloading of the airplane catering, the vehicle is switched

off so that no energy is consumed and hence this process is not included in the work cycle of

the lifting system. The second section of the work cycle begins after the completion of the

loading of the given airplane's catering and comprises lowering the catering container and

retracting all extended parts. Hence, in this section the front platform tongue is retracted first,

after which the front platform is moved back, the catering container is lowered, and finally, the

support legs are retracted.

In the real operation of the lifting system, the catering container always has to reach its

maximum height to be parallel to the airplane door, which means that the telescopic cylinder

actuators have to be fully extended every time whereas the other functions are not always fully

actuated. The lateral displacement of the front platform and the extension of the front platform

tongue depend on the relative position between the container and the airplane door and thus

they are only actuated until the airplane door is reached. Similarly, the support legs are also

extended until they dock on the ground whereby their strokes may vary each time depending

on the evenness of the ground and on the weight of the vehicle. For simplicity, it is assumed

in the defined work cycle of the lifting system that all functions are fully actuated and reach

their maximum values.

The realistic energy consumption of the lifting system can be determined by simulating its

hydraulic circuit. For this purpose, hydraulic measurement data was recorded for the lifting

system by DOLL Fahrzeugbau GmbH, which will serve as the reference for the simulation

model. The measurement was carried out for a complete work cycle, as defined previously, for

the lifting system of the reference diesel truck. As the electric prototype truck was not yet in

existence at the time of the measurements, it was not possible to take measurements of its

lifting system.

In the measurement, all functions were operated until they reached their maximum and the

fluid pressure and volumetric flow rate at the outlet of the pump were recorded. Figure 2-7

shows the plots of the recorded measurement data. Table 2-3 provides a breakdown of the

defined work cycle based on measurement data after being analyzed and separated into

individual cycles.

Since all functions are operated to the end, the strokes of the hydraulic actuators are already

known. The duration of every individual operated function can be extracted from the

measurement dataset and taken as input for the simulation mode and based on these values,

the speed profiles of the hydraulic actuators can be generated while the data of the pressure

and volumetric flow rate are only used to validate the simulation model of the lifting system.

The idling represents the intervals between the operating functions, during which no action is

performed but the pump is running and works at the minimum pressure of the system. The

typical sequence of functions is fixed in every work cycle. However, the idling time between

the operating functions and thus the duration of the overall work cycle depends on the operator.

For simplicity, equal intervals between functions are assumed in the simulation model.

Figure 2-7: Hydraulic measurements of the lifting system (Source: DOLL Fahrzeugbau

GmbH)

Table 2-3: Characteristics of the defined work cycle of the lifting system based on

measurement data

Lifting Cycle

Lowering Cycle

Function

Stroke

[m]

Duration

[s]

Pressure

[bar]

Flow

[L/min]

Stroke

[m]

Duration

[s]

Pressure

[bar]

Flow

[L/min]

Support legs

0.38

110

0.38

170

Container

1.412

115/145*

1.412

Front platform

1.46

Front platform

tongue

0.6

8.5

0.6

5.5

Idling

* Cylinder first/second stage

The lifting/lowering of the container is the main function of the lifting system. The lifting process

requires high values of pressure and volumetric flow rate while the lowering process is carried

out by the container's own weight so that no additional pressure has to be applied by the pump.

Hence, the pump is running at the minimum pressure of the system during lowering. However,

it should be noted that the measurement for the container were carried out without any loads.

A higher pressure is therefore required when the container is loaded.

Figure 2-8 shows the proportional time distribution of the measured work cycle. The complete

duty cycle of the lifting system takes 222 seconds, including the lifting and lowering cycles. In

terms of time, the function of lifting and lowering the container accounts for the largest

proportion at 54.1%, followed by the function of the front platform at 20.7% whereas other

functions have relatively small proportions.

Figure 2-8: Time distribution according to the lifting functions

Since all functions were fully operated until their maximum values were reached during the

measurements, it is supposed that this is the maximum possible time that a work cycle of lifting

system can take. However, it should be noted that, in practice, the speed of movement of the

hydraulic fluid in the cylinder actuators can vary depending on its temperature, which can lead

to slight differences in the durations of the functions of the lifting system.

10.8%

9.0%

54.1%

20.7%

5.4%

Idling

Support legs

Container

Front platform

tongue

3 Modeling and Simulation of the Catering Lift Truck

In this work, a simulation model was developed with MATLAB/Simulink to determine the energy

consumption and energy efficiency of the two trucks under consideration. The simulation

results are the basis for further analyses to show the competitiveness of the electrified truck in

all relevant aspects. The model is based on mapping all types of energy flows involved

(mechanical, electrical, and hydraulic) and significant energy losses in all relevant components

of the catering lift truck. For this purpose, the mechanics of the powertrain as well as the

hydraulic circuit of the lifting system are modeled with their respective functions. The input to

the model is the predefined work cycles of the catering lift truck that are presented in Chapter

2.3. In addition to the structure of the simulation model, a comprehensive tool was developed

to parameterize the individual models and to analyze the simulation results. Another tool

enables the analysis of the measurement data and the extraction of individual cycles from a

series of measurements. The simulation results were validated by comparison to the average

real diesel and electricity consumption of the two trucks.

The simulation model ensures an objective and reproducible comparison of different drive

concepts under identical boundary conditions. In addition, it offers the possibility of examining

the influence of significant variables on energy consumption and efficiency. However, given

the complexity of the system to be modeled, it is necessary to reduce the modeling effort

through appropriate simplification, as long as this is compatible with the model’s purpose while

maintaining the required accuracy of the results. In the following, the individual simulation

models of the driving and lifting systems are presented separately to consider the consumption

of the individual systems in more detail.

3.1 Driving System Model

The driving system model calculates the driving energy requirement of the catering lift truck

based on the measured driving cycles. The basic structure of the model for the two trucks

under consideration is shown in Figures 3-1 and 3-2. The model is based on backward

calculations, described in Chapter 1.2.3, and thus no driver model is required as mechanical

and electrical losses and losses inside the drive are represented in the efficiency of the

individual components. The input to the model is the speed and acceleration data of the

respective driving cycle. These are used to calculate the driving resistance force, which

opposes the movement of the vehicle, at any given point in time.

The model is the same for both trucks except for the drive model, which is represented by the

diesel engine model in the diesel truck and the corresponding electric drive model, including

the electric motor, the power electronics and the traction battery, in the electric truck.

Figure 3-2: Schematic structure of the simulation model of the electric truck driving system

Figure 3-1: Schematic structure of the simulation model of the diesel truck driving system

𝒂

Driving Cycle

𝒗

Vehicle

Wheels

Differential

Transmission

𝑷

Diesel

Engine

Consumpti

𝝎𝒎𝒐𝒕𝒐𝒓

𝑻𝒎𝒐𝒕𝒐𝒓

𝒗

𝑭𝒘

𝐺𝑒𝑎𝑟 𝑛𝑟.

Shifting Map

Consumption

Driving

Resistance

Acceleration

Resistance

Gradient

Resistance

Rolling

Resistance

Air

Resistance

Electric Drive

Motor/

Generator

Power

Electronics

Battery

𝒂

Driving Cycle

𝒗

Vehicle

Wheels

Differential

Transmission

𝑷

𝝎𝒎𝒐𝒕𝒐𝒓

𝑻𝒎𝒐𝒕𝒐𝒓

𝒗

𝑭𝒘

𝐺𝑒𝑎𝑟 𝑛𝑟.

Shifting Map

Consumption

Battery SOC

Driving

Resistance

Acceleration

Resistance

Gradient

Resistance

Rolling

Resistance

Air

Resistance

Recuperation

Power

The total driving resistance force 𝐹𝑤 is composed of the acceleration resistance force 𝐹𝑎𝑐𝑐, the

gradient resistance force 𝐹𝑔, the rolling resistance force 𝐹𝑟 and the air resistance force 𝐹𝑎

[Mitschke & Wallentowitz, 2014] [Naunheimer & Bertsche, 2007]:

𝐹𝑤= 𝐹𝑎𝑐𝑐 + 𝐹𝑔 + 𝐹𝑟 + 𝐹𝑎

(1)

The acceleration resistance force is described in Newton’s second law of motion as the force

that has to be applied to a body to be accelerated. It comprises the inertia in the direction of

movement of the vehicle (translational part 𝐹𝑎𝑐𝑐𝑡𝑟𝑎𝑛), as well as the rotational resistance of the

rotating components (rotational part 𝐹𝑎𝑐𝑐_𝑟𝑜𝑡) such as the motor shaft, transmission, differential

and vehicle wheels. Thus, the acceleration resistance force results in:

𝐹𝑎𝑐𝑐 = 𝐹𝑎𝑐𝑐𝑡𝑟𝑎𝑛 + 𝐹𝑎𝑐𝑐𝑟𝑜𝑡

(2)

The translational part of the acceleration resistance force can be represented as:

𝐹𝑎𝑐𝑐𝑡𝑟𝑎𝑛 =𝑚𝑣⋅𝑎

(3)

where 𝑚𝑣 is the total vehicle mass and 𝑎 is the vehicle acceleration.

The rotational part of the acceleration resistance force is composed of the respective individual

inertia of the rotating components. Since these are usually difficult to determine, the rotational

acceleration resistance force is often described by a dimensionless rotational mass addition

factor

𝜆 depending on the current gear ratio [Hoepke & Breuer, 2016]. Hence, the acceleration

resistance force can be simplified to:

𝐹𝑎𝑐𝑐 = 𝜆⋅ 𝐹𝑎𝑐𝑐𝑡𝑟𝑎𝑛

(4)

The factor is given as a function of the transmission ratio in some literatures, such as [Mitschke

& Wallentowitz, 2014] and [Naunheimer & Bertsche, 2007].

The gradient resistance force is calculated by using the angle of inclination of the road 𝑝ℎ𝑖 and

the weight of the vehicle:

𝐹𝑔= 𝑚𝑣⋅𝑔⋅𝑠𝑖𝑛(𝑝ℎ𝑖)

(5)

Where 𝑔 is the acceleration due to gravity. Since the catering lift truck only drives on flat

ground at the airport apron and the road leading to it, the gradient resistance is considered

irrelevant in this application and will therefore be neglected in the simulation.

The rolling resistance force represents the resistance forces that act on the wheels when rolling

on the road surface. It is an almost linear function of the wheel load that can be described with

the dimensionless rolling resistance coefficient 𝑓𝑟 [Mischke & Wallentowitz, 2014] [Naunheimer

& Bertsche, 2007]. Thus, the total rolling resistance force is calculated as:

𝐹𝑟= 𝑚𝑣⋅𝑔⋅𝑓𝑟 ⋅

cos

(𝑝ℎ𝑖)

(6)

The angle of inclination is usually neglected for roads with uphill/downhill gradients below 10%.

The rolling resistance coefficient is mainly dependent on the tire conditions, road surface and

driving speed. However, in the lower speed range, it can be considered as a constant for

simplification. Typical values for the rolling resistance coefficient depending on the road

surface for speeds of less than 60 km/h can be found in [Naunheimer & Bertsche, 2007].

Air resistance force describes the resistance force of the ambient air that flows around the

moving vehicle and through it. Hence, the air resistance force is a result of the pressure

difference in the direction of flow, the friction on the surface of the vehicle, and the internal flow

resistance. It can be calculated as follows:

𝐹𝑎= 1

2⋅𝐴⋅𝑐𝑤⋅𝑣2⋅ 𝜌𝑎

(7)

where 𝑣 is the vehicle driving speed, 𝐴 is the vehicle cross-sectional area, 𝑐𝑤 is the drag

coefficient, and 𝜌𝑎 is the air density. The values of the drag coefficient and cross-sectional area

for trucks can be derived from the literature.

Additional partial resistances can also occur due to the toe-in/toe-out or when cornering.

However, these are usually not considered because their effect is negligible.

The required torque at the vehicle wheels 𝑇𝑤 can then be calculated from the driving resistance

force using the dynamic wheel radius 𝑟𝑑𝑦𝑛 [ Breuer & Rohrbach-Kerl, 2015].

𝑇𝑤= 𝐹𝑤⋅𝑟𝑑𝑦𝑛

(8)

Similarly, the wheel’s angular velocity 𝜔𝑤 can be calculated from the vehicle velocity and the

dynamic wheel radius.

𝜔𝑤= 𝑣

𝑟𝑑𝑦𝑛

(9)

Since it is assumed that the speed and torque are the same on all wheels, the differential is

modeled by only taking the energy loss into account. The transmission is modeled with the

help of a shifting map and the gear step is selected depending on the speed of the transmission

output. The transmission ratio is then specified for the currently selected gear. Subsequently,

the required torque and speed at the engine, 𝑇𝑚𝑜𝑡𝑜𝑟 and 𝜔𝑚𝑜𝑡𝑜𝑟, are determined based on the

differential ratio 𝑖𝑑𝑖𝑓𝑓 and the respective ratio of the current transmission gear 𝑖𝑡𝑟𝑎𝑛𝑠 taking the

efficiencies of the differential 𝜂𝑑𝑖𝑓𝑓 and the transmission 𝜂𝑡𝑟𝑎𝑛𝑠 into account.

𝑇𝑚𝑜𝑡𝑜𝑟 = 𝑇𝑤

𝑖𝑑𝑖𝑓𝑓⋅ 𝜂𝑑𝑖𝑓𝑓⋅𝑖𝑡𝑟𝑎𝑛𝑠⋅ 𝜂𝑡𝑟𝑎𝑛𝑠

(10)

𝜔𝑚𝑜𝑡𝑜𝑟 = 𝜔𝑤⋅𝑖𝑑𝑖𝑓𝑓⋅𝑖𝑡𝑟𝑎𝑛𝑠

(11)

The power requested by the engine 𝑃𝑚𝑜𝑡𝑜𝑟 can be given by:

𝑃𝑚𝑜𝑡𝑜𝑟 = 𝑇𝑚𝑜𝑡𝑜𝑟 ⋅𝜔𝑚𝑜𝑡𝑜𝑟

(12)

In the overall vehicle simulation, modeling of the engine is of central importance, since it is

responsible for converting the primary energy and has the greatest energy loss in the vehicle.

However, the realizable level of detail of the modeling is determined by the availability of data

for the parameterization.

For the diesel truck, the diesel engine is modeled using its consumption map, which indicates

the specific fuel consumption of the engine 𝐶𝑜𝑛𝑠𝑠𝑝𝑒𝑐 at each operating point relative to the

engine speed. The total fuel demand 𝐶𝑜𝑛𝑠𝑓𝑢𝑒𝑙 for an input driving cycle can then be determined

as a function of the engine power, the specific fuel consumption, and the diesel density 𝜌𝑑𝑖𝑒𝑠𝑒𝑙

over the time period 𝑡1 to 𝑡2.

𝐶𝑜𝑛𝑠𝑓𝑢𝑒𝑙 = ∫(𝑃𝑚𝑜𝑡𝑜𝑟 ⋅𝐶𝑜𝑛𝑠𝑠𝑝𝑒𝑐

𝜌𝑑𝑖𝑒𝑠𝑒𝑙 )𝑑𝑡

𝑡2

𝑡1

(13)

However, the purpose of the simulation model in this work is to consider the overall energy

balance of the whole vehicle to compare the two trucks being considered. Therefore, the total

power demand of the diesel truck 𝑃𝑖𝑛𝑑 is determined as the input power to the engine using

the consumption map of the engine:

𝑃𝑖𝑛𝑑= 𝑃𝑚𝑜𝑡𝑜𝑟 ⋅𝐶𝑜𝑛𝑠𝑠𝑝𝑒𝑐 ⋅𝐻𝑢

𝜌𝑑𝑖𝑒𝑠𝑒𝑙

(14)

where 𝐻𝑢 is the heating value of diesel. The total energy consumed 𝐸𝑖𝑛𝑑 can then be

determined in an integrative manner:

𝐸𝑖𝑛𝑑= ∫𝑃𝑖𝑛𝑑𝑑𝑡

𝑡2

𝑡1

(15)

For the electrified catering lift truck, the efficiency map of the electric motor is used to determine

the motor efficiency at the respective operating point. The total power demand of the electric

truck 𝑃𝑖𝑛𝑒 is subsequently given by the motor power that is calculated above, the associated

electric motor efficiency 𝜂𝑚𝑜𝑡𝑜𝑟𝑒, and the efficiency of the power electronics including the

battery 𝜂𝐴𝐶/𝐷𝐶:

𝑃𝑖𝑛𝑒=𝑃𝑚𝑜𝑡𝑜𝑟

𝜂𝑚𝑜𝑡𝑜𝑟𝑒⋅𝜂𝐴𝐶/𝐷𝐶

(16)

Consequently, the total energy consumption of the electric truck 𝐸𝑖𝑛𝑒 can be similarly

determined by integration:

𝐸𝑖𝑛𝑒= ∫𝑃𝑖𝑛𝑒𝑑𝑡

𝑡2

𝑡1

(17)

Since the simulation model focuses on modeling the energy consumption of the vehicle

components, the battery model does not represent the function of the traction battery, but only

calculates the state of charge of the battery. The current state of charge of the battery (SOC)

𝑏𝑎𝑡𝑆𝑂𝐶 can be derived from the energy consumption, the initial capacity of the battery at the

start of the rout 𝐶𝑎𝑝𝑠𝑡𝑎𝑟𝑡, and the total capacity available when the battery is fully charged

𝐶𝑎𝑝𝑡𝑜𝑡𝑎𝑙.

𝑏𝑎𝑡𝑆𝑂𝐶 =𝐶𝑎𝑝𝑠𝑡𝑎𝑟𝑡 − 𝐸𝑖𝑛𝑒

𝐶𝑎𝑝𝑡𝑜𝑡𝑎𝑙 .100%

(18)

In addition, the applied output power 𝑃𝑜𝑢𝑡 is also determined. This is the power provided to the

wheels to drive the vehicle, i.e., the usable power without losses. From this, the output energy

𝐸𝑜𝑢𝑡 is derived.

𝑃𝑜𝑢𝑡 = 𝐹𝑤⋅ 𝑣

(19)

𝐸𝑜𝑢𝑡 = ∫𝑃𝑜𝑢𝑡𝑑𝑡

𝑡2

𝑡1

(20)

It should be noted that the model does not take into account the energy consumption of

auxiliary units such as the cooling system inside the container or the heating and cooling in the

driver’s cab, these functions are not operated continuously and therefore do not represent a

fixed part of the work cycle.

The model is parametrized according to the respective truck characteristics that are described

in Chapter 2.1 and Appendix A. A summary list of the parameters used in the simulation model

is provided in Table 3-1. Literature values are used for parameters for which data are not

available. Where the values used for rolling and drag resistance correspond to the average

values from [Mitschke & Wallentowitz, 2014] [Naunheimer & Bertsche, 2007]. While the values

for the efficiency of the differential and transmission were assumed based on the value ranges

given in [Naunheimer & Bertsche, 2007], [Lechner & Naunheimer, 1999] and [Jerratsch, 2021].

Due to the additional weight of the battery, the electric truck is slightly heavier than the diesel

truck, despite the omission of the diesel tank and the lightness of the electric motor compared

to the diesel engine.

The information on the payload is based on experience, as the weight of the catering cannot

be determined exactly. However, the truck travels always loaded with trolleys, which are either

packed with catering on the way to the airplane or with waste on the way back. The number

and weight of the trolleys are usually known, but the weight of the load inside cannot be

determined. Therefore, an average payload of 3,000 kg is considered for the simulation based

on estimations.

Table 3-1: Key parameters of the driving simulation model

Parameter

Diesel Truck

Electric Truck

Vehicle weight [kg]

16,174

17,234

Simulated pay load [kg]

3,000

Motor power [kW]

142

138

Motor torque [Nm]

809

780

Motor max. rotation speed [1/min]

2,000

2,500

Motor min. rotation speed [1/min]

800

–

Battery capacity [kWh]

–

113

Vehicle frontal area [m2]

9.3

Drag coefficient

0.53

Rolling resistance coefficient

0.01

Dynamic tire radius [m]

0. 5097

Differential ratio

9.49

Differential efficiency

98%

Transmission gear ratio

[3.49, 1.86, 1.41, 1.00, 0.75]

Transmission efficiency

92%

Rotational mass addition factor

[1.06, 1.04, 1.035, 1.03, 1.025, 1.02]

Figure 3-3 shows the time course of the required driving power of both trucks compared to the

vehicle speed for the driving cycles that are previously described in Chapter 2.3.1. Based on

the findings summarized in the graphs, it is evident that the electric truck has lower levels of

power demand across all driving cycles and the diesel truck requires almost twice the amount

of power needed by the electric truck to drive the same route. However, the difference depends

on the vehicle speed profile as the biggest differences occur at high accelerations. When

comparing at constant speeds, the difference decreases slightly with increasing speed and

increases slightly with lower speed, since the diesel engine is more efficient at high speeds,

whereas its efficiency decreases at low speeds. In contrast, the efficiency of the electric motor

hardly drops at lower speeds.

When considering the peak power, the diesel truck has a peak power of up to 400 kW, while

the peak power for the electric truck hardly exceeds 200 kW. Otherwise, the power required

for both vehicles is usually in the range of the nominal motor power.

The recovered braking power can be seen in the negative area which represents the

regenerated power supplied to the battery when the motor is operating in the generator mode.

Figure 3-3: Simulation results of the power demand of the driving model

-20

-10

-200

-100

100

200

300

400

500

0 100 200 300 400 500 600

Velocity [km/h]

Power [kW]

Time [s]

Driving cycle 1

Power of diesel truck Power of electric truck Velocity

-20

-10

-200

-100

100

200

300

400

500

0 200 400 600 800 1000 1200

Velocity [km/h]

Power [kW]

Time [s]

Driving cycle 2

Power of diesel truck Power of electric truck Velocity

-20

-10

-300

-200

-100

100

200

300

400

500

0 400 800 1200 1600 2000 2400

Velocity [km/h]

Power [kW]

Time [s]

Driving cycle 3

Power of diesel truck Power of electric truck Velocity

Table 3-2 compares the simulation results for the consumption of both trucks, which clearly

show the expected effect of electrification on consumption. It is noticeable that the electrified

truck requires significantly less energy than the diesel truck and, on average, approximately

two thirds of the energy consumption can be saved through electrification. As shown, the

energy savings in driving cycles 1 and 2 are almost the same, while the energy saving is slightly

lower in driving cycle 3. This could be attributed to the smaller proportions of acceleration and

braking maneuvers in addition to the higher average speed of this cycle, as can be seen in

Figure 2-5 and Table 2-1 in Chapter 2.3.1. In this cycle, there is thus less braking energy to

recover. Furthermore, the diesel engine operates with a relatively higher efficiency range at

high average speeds. The total absolute consumption difference at end of the three driving

cycles is 65.94%.

Table 3-2: Simulation results of the driving model

Route

Diesel truck

consumption

[kWh]*

Electric truck

consumption

[kWh]

Energy Saving

Driving cycle 1

11.63

3.82

67.15%

Driving cycle 2

25.03

8.15

67.44%

Driving cycle 3

42.93

15.14

64.73%

Total

79.59

27.11

65.94%

*The energy equivalence (calorific value) of 1 liter of diesel considered is 9.79 kWh/L

Figure 3-4 shows the average energy consumption per 100 km whereby all consumption

values are shown in liters of diesel for comparison. For the purpose of illustration, the

consumption values of the electric truck are also shown without recuperation. As shown, the

diesel truck has a significantly higher consumption rate in all driving cycles, which amounts to

almost three times that of the electrified truck. From the results, it is also evident how the

recuperation of braking energy significantly reduces the consumption of the electric truck.

However, the results also show that even when not considering the recuperation, there is a

considerable difference in consumption between the two trucks which is due to the different

characteristics of their driving technologies as the electric motor has a higher degree of

efficiency compared to the diesel engine. In addition, the diesel engine consumes a small

amount of fuel when idling even though the truck is not moving. Based on experience, it is

assumed that the diesel engine has a consumption of 2.6 L/h when idling. In comparison, the

electrified truck does not consume any energy when idling.

At the same time, Figure 3-4 shows the variation in consumption rate caused by the individual

driving cycles, which reflects the impact of driving modes on energy consumption. In driving

cycles 1 and 2, both trucks have higher consumption rates compared to driving cycle 3

because these driving cycles have higher acceleration maneuvers, which results in higher

consumption. On the other hand, the amount of recovered energy in cycles 1 and 2 should

also be higher because of the many abrupt stops. Therefore, it can be observed that the

influence of the increased proportion of acceleration on the consumption of the diesel truck is

greater than on the consumption of the electric truck. As for the third driving cycle, which has

the lowest acceleration proportion, and hence the lowest average power demand, both trucks

have the lowest consumption rate in this cycle and hence the proportion of recovered energy

is also not as high compared to other cycles.

In total, the simulated consumption rate based on the entire driving cycles is 29.93 liters per

100 km for the diesel truck, while it is 10.20 liters per 100 km for the electric truck with recovery

and 13.55 liters per 100 km without taking recovery into account.

Figure 3-4: Simulation results of the consumption rate of the driving system

For further examination and a more detailed comparison of the energy consumption of the two

trucks, the distribution of energy consumption according to driving modes, as defined in Table

2-2 in Chapter 2.3.1, is shown in Figure 3-5. Here, the energy consumption value is calculated

for each of the four driving modes for the total driving cycles. As shown, the bulk of the truck's

energy consumption is in the acceleration phase, which is consistent with the nature of the

truck's driving cycles. Almost half of the energy consumption in the acceleration and cruise

modes can be saved by using the electrified truck, while in idle and braking modes, the entire

energy consumption can be saved since the electric motor is turned off and therefore

consumes no energy. Furthermore, energy is recovered in the braking phases and added to

the system. This is represented by the negative energy value in Figure 3-5.

32.11 32.86

27.97 29.93

14.47 15.15

12.55 13.55

10.55 10.70 9.86 10.20

Cycle 1 Cycle 2 Cycle 3 Total

Consumption [L/100 km]

Diesel truck Electric truck without recuperation Electric truck with recuperation

Figure 3-5: Simulation results of energy consumption of the driving system according to the

driving modes

From the results, it can be deduced that the consumption values heavily depend on driving

behavior and the traffic environment as the speed profiles of different driving cycles vary

greatly, even for the same route. The results indicate that electrification is more beneficial in

driving cycles of high acceleration and deceleration frequencies, where the advantage of the

electric motor in energy saving and recuperation stands out.

It should be noted, however, that the simulation results only represent the consumption in the

vehicle (TTW) since the energy consumption for the generation and provision of electricity and

diesel fuel (WTT) is not considered in the simulation model but will be taken into account later

when discussing the total demand for primary energy in the life cycle analysis.

In addition to the energy consumption, the battery SOC of the electric truck is also simulated,

and the results are shown in Figure 3-6 for both with and without the recuperation. It is assumed

that the electric truck starts driving with a 90% battery charge since when the battery is fully

charged, the energy recovered during braking cannot be fed back into the battery. The truck

completes the three driving cycles in succession without being charged in between. As Figure

3-6 shows, the SOC level with recuperation is always above the SOC level without

recuperation. The curve of the SOC with recuperation clearly indicates that the discharging

process is repeatedly interrupted by short charging phases, which represent the energy

recovered during braking. This results in permanently lower SOC exhaustion and thus provides

a consumption advantage. At the end of the three driving cycles, the battery SOC is about

58.1%, while it is about 66% when considering recharging through recovery.

2.62

49.41

20.87

6.70

0.00

25.76

10.28

-8.94

-10

Idle Acceleration Cruise Braking

Energy [kWh]

Diesel truck Electric truck

Figure 3-6: Simulation results of the battery SOC of the driving system of the electric truck

0 100 200 300 400 500 600

Velocity [km/h]

SOC [%]

Time [s]

Driving cycle 1

SOC [%] SOC with recovery[%] Velocity

0 200 400 600 800 1000 1200

Velocity [km/h]

SOC [%]

Time [s]

Driving cycle 2

SOC [%] SOC with recovery[%] Velocity

0 400 800 1200 1600 2000 2400

Velocity [km/h]

SOC [%]

Time [s]

Driving cycle 3

SOC [%] SOC with recovery[%] Velocity

Table 3-3 presents the savings achieved in SOC as well as in energy consumption through

recuperation. At the end of the three driving cycles, 31.48% of the battery charge should have

been consumed but thanks to the recuperation, 7.63% was saved. The highest savings were

achieved in driving cycle 2 and the lowest savings in driving cycle 3. This is specifically to be

expected given the braking proportion on these cycles. The total average energy savings of all

driving cycles is 24.24%. The total saving percentage is considered reasonable and is

consistent with the expected range of values based on the literature. Similar values have

already been found for electric heavy vehicles in other studies such as [Lajunen, 2014] and

[Van Sterkenburg et al., 2011].

Table 3-3: Energy saving by recuperation in the electric truck

Route

SOC without

Recuperation

SOC with

Recuperation

SOC

Difference

Energy

Recovery

Driving cycle 1

4.63%

3.38%

1.25%

27.00%

Driving cycle 2

10.21%

7.21%

3.00%

29.38%

Driving cycle 3

16.64%

13.26%

3.38%

20.31%

Total

31.48%

23.85%

7.63%

24.24%

3.2 Lifting System Model

In the diesel truck, all functions of the lifting system are powered by the diesel engine. The

diesel engine drives an axial pump, which pumps oil from the tank into the hydraulic circuit of

the lifting system. Figure 3-7 shows the schematic representation of the basic lifting hydraulic

circuit. The oil flows from the pump through the control valves to the respective cylinder

actuator. The cylinder actuator converts the hydraulic energy back into mechanical energy as

the oil causes the piston rod to extend out of the cylinder tube and thus the respective function

is performed. The hydraulic circuit has a load-sensing control valve to keep the system

pressure relative to the operated function.

In the electrified truck, the functions of the container and the support legs remain hydraulically

operated in the same way. However, the hydraulic pump is powered by the vehicle’s electric

motor instead of the diesel engine while the functions of the platform and platform tongue are

performed electromechanically by their own associated electric drives. Therefore, these two

functions are excluded from the hydraulic circuit in the model of the electric truck lifting system

and are simulated in a separate model. Other than that, the model of the hydraulic circuit

remains the same for both trucks.

Figure 3-7: Simplified hydraulic circuit of the catering lift truck lifting system (Adapted from

the original hydraulic circuit diagram provided by DOLL Fahrzeugbau GmbH)

Accordingly, when modeling the lifting system, in addition to modeling the hydraulic circuit,

models of the vehicle engine as well as the connecting auxiliary gear also have to be included.

The structure of the simulation model of the lifting system of both trucks is shown in Figures 3-

8 and 3-9.

Figure 3-8: Schematic structure of the simulation model of the diesel truck lifting system

Figure 3-9: Schematic structure of the simulation model of the electric truck lifting system

The hydraulic model is simplified as much as possible so that all effects that are expected to

influence the behavior of the system in terms of energy consumption are included, whereby

the following components are considered: control valves, cylinder actuators, load sensing

valve, and pump. The pressure losses are also taken into account. Components that are not

relevant to the energy balance – such as the safety mechanisms – are not considered in the

model, since these are only triggered in emergency situations and not during the working

process. Since the functions of the lifting system are only operated separately, there is no

dependency between the individual actuators, and hence every function of the lifting system

will be simulated individually, and the successive individual performances of the actuators are

combined into a complete cycle.

Similar to the driving model, the purpose of the calculation in this model is to determine the

power balance and energy consumption of the lifting system. However, the calculation is based

on hydraulic data and not on motion data as in the drive system model. The input data to the

model represents the system settings data, which determines the opening degree of the

hydraulic control valves. Based on this, the volumetric flow distribution and the pressure build-

up are determined, which represent the power flows between the control valves and actuators.

The motion and associated speed profiles for the individual actuator cylinder are generated by

the difference between the incoming and outgoing hydraulic power flows on both sides of the

actuator. The main part of the model for the hydraulic circuit represents the valve-cylinder

combination. This is illustrated according to [Jelali & Kroll, 2003] [Will & Gebhardt, 2008] in

Figure 3-10 for the functions of the support legs, platform, and platform tongue, and in Figure

3-11 for the function of lifting/lowering the catering container.

Figure 3-10: Schematic representation of the combination of the 3-port/4-way directional

control valve and double-acting cylinder actuator with variable definitions (for functions of the

support legs, platform and platform tongue)

For the configuration of the 3/4 directional control valve and double-acting cylinder shown in

Figure 3-10, the inlet and outlet volumetric flow rates of the cylinder, 𝑄𝐴 and 𝑄𝐵, are considered

to be the same as those of the valve and are therefore determined using the valve

characteristic map based on the pressure difference between the valve ports corresponding to

the flow direction. 𝑄𝐴 is determined based on the inflow pressure difference between the valve

ports S (pump-side port) and A/B. Since this pressure difference is constant due to the use of

a feed regulator in the valve, the value of 𝑄𝐴 is also constant and is specified based on the

𝑚

𝐿

𝑝

𝐴

𝑝

𝐵

𝑄𝐴

𝐴𝑃𝐵

𝐴𝑃𝐴

𝑄𝐵

𝐹

𝐿

𝑥𝑃,𝑥𝑃

󰇗 ,𝑥𝑃

󰇘

𝐹𝑅

𝐴

𝐵

𝑆

𝑇

valve setting data. 𝑄𝐵 is determined based on the return pressure difference between valve

ports A/B and T (tank-side port).

The cylinder actuator is modeled considering the dynamic behavior of the cylinder piston,

which governs the load motion [Jelali & Kroll, 2003] [Will & Gebhardt, 2008]. Hence, the

differential equation of motion of the cylinder piston can be formulated as follows:

(𝑚𝐿+𝑚𝑃)⋅𝑥𝑃

󰇘 =𝑝𝐴⋅𝐴𝑃𝐴 − 𝑝𝐵⋅𝐴𝑃𝐵 −𝐹𝐿−𝐹𝑅

(21)

where 𝑚𝐿 is the mass of the load to be moved, 𝑚𝑃 and 𝑥𝑃

󰇘 are the mass and acceleration of

the cylinder piston respectively, 𝑝𝐴 and 𝑝𝐵 are the pressures in the respective cylinder

chamber, which are equal to the pressures in the pipelines A and B, and 𝐴𝑃𝐴 and 𝐴𝑃𝐵 are the

areas of the piston on sides A and B. 𝐹𝐿 denotes the force of the load to be moved. The term

𝐹𝑅 describes the speed-dependent friction force in the hydraulic cylinder. It can be modeled

using the Tustin friction model, which is a form of the Stribeck curve [Beater, 1999] [Tang et

al., 2002]:

𝐹𝑅=𝑠𝑖𝑔𝑛(𝑥𝑃

󰇗)[𝐹𝐶+𝐹𝑆⋅exp(−|𝑥𝑃

󰇗|

𝑐𝑠)]+𝐹𝐷

(22)

where 𝑥𝑃

󰇗 is the velocity of the cylinder piston that represents the integration of the acceleration

of the cylinder piston, 𝐹𝐶 is the Coulomb sliding friction force (kinetic friction), 𝐹𝑆 is the stiction

friction force (maximum static friction force), 𝐹𝐷 is the viscous friction force and 𝑐𝑠 is the Stribeck

velocity [Jelali & Kroll, 2003]. The stiction and sliding friction forces represent the frictional

resistance between the cylinder piston and the cylinder body and can be estimated with the

help of normal force and friction coefficients [Sommer et al., 2014]:

𝐹𝑆=𝜇𝑠⋅𝐹𝑛

(23)

𝐹𝐶=𝜇𝑘⋅𝐹𝑛

(24)

where 𝐹𝑛 is the normal force of the cylinder piston on the friction surface, which results from its

own weight. Reference values for the stiction and sliding friction coefficients 𝜇𝑠 and 𝜇𝑘 can be

found in the literature for a lubricated steel-steel pairing [Lackmann, 2007] [Gieck & Gieck,

2019]. The viscous friction force represents the damping resistance that arises because of the

movement of the piston in the oil:

𝐹𝐷=𝑐⋅𝑥󰇗𝑃

(25)

The coefficient of that viscous friction force 𝑐 can be determined from experimental data [Jelali

& Kroll, 2003] or estimated based on the geometry of the cylinder piston for laminar flow in a

pipe with an annular cross-section [Gieck & Gieck, 2019]. In this work, it was estimated based

on the available data.

The total change of the volumetric flow rate in the cylinder chambers 𝑄𝑡𝐴 and 𝑄𝑡𝐵 can be

calculated from the balance of the volumetric flow rate in the respective cylinder chamber

(neglecting leakage flows):

𝑄𝑡𝐴=𝑄𝐴−𝑄𝑉𝐴

(26)

𝑄𝑡𝐵= 𝑄𝑉𝐵−𝑄𝐵

(27)

where 𝑄𝐴 and 𝑄𝐵 are the volumetric flow rates in the cylinder throttle points that are considered

the same volumetric flow rates of the valve. 𝑄𝑉𝐴 and 𝑄𝑉𝐵 denote the volumetric flow rate

differences due to volume change in the respective cylinder chamber, and can be calculated

as follows:

𝑄𝑉𝐴=𝐴𝑃𝐴⋅𝑥󰇗𝑃

(28)

𝑄𝑉𝐵=𝐴𝑃𝐵 ⋅𝑥󰇗𝑃

(29)

The total change in the volumetric flow rate, in conjunction with the compressibility of the

hydraulic oil, determines the pressure build-up in the respective cylinder chamber volume.

𝑝𝐴

󰇗 = 𝑄𝑡𝐴⋅𝐾

𝑉𝐴

(30)

𝑝𝐵

󰇗 = 𝑄𝑡𝐵⋅𝐾

𝑉𝐵

(31)

Here, 𝐾 is the bulk modulus for oil and 𝑉𝐴 and 𝑉𝐵 are the volumes of the respective cylinder

chambers, which can be expressed as:

𝑉𝐴= 𝑥𝑃⋅𝐴𝑃𝐴+𝑉0𝐴

(32)

𝑉𝐵= (𝑥𝑝_𝑚𝑎𝑥−𝑥𝑃)⋅𝐴𝑃𝐵 +𝑉0𝐵

(33)

where 𝑥𝑃 and 𝑥𝑝_𝑚𝑎𝑥 are respectively the current and maximum stroke of the cylinder piston

and 𝑉0𝐴 and 𝑉0𝐵 are the initial chamber volumes when the piston is fully extended or fully

retracted.

For the function of lifting/lowering the catering container, the two 2-stage telescopic cylinders

and the corresponding one-directional control valves are modeled as shown in Figure 3-11.

Since the oil flows in one direction through the control valve, there is only the inlet volumetric

flow rate 𝑄𝐴 and this is determined in the same way using the valve characteristic map and

setting data.

Figure 3-11: Schematic representation of the combination of the 2-port/1-way directional

control valve and single-acting telescopic cylinder actuator (for the function of lifting/lowering

the container)

The individual stages of the telescopic cylinder extend one after the other while hydraulic oil is

pumped to one side of the cylinder, so each stage can be modeled as a single-acting cylinder

[Findeisen, 2006] [Will & Gebhardt, 2008]. The model equations of the telescopic cylinder are

subsequently given as follows:

(𝑚𝐿+2⋅𝑚𝑃1,2)⋅𝑥𝑃

󰇘 =𝑝𝐴⋅2⋅𝐴𝑃𝐴1,2 −𝐹𝐿− 𝐹𝑅

(34)

𝑄𝑡𝐴 =𝑄𝐴− 2 ⋅ 𝑄𝑉𝐴

(35)

𝑄𝑉𝐴 =𝐴𝑃𝐴1,2 ⋅𝑥󰇗𝑝

(36)

𝑝𝐴

󰇗 = 𝑄𝑡𝐴⋅𝐾

2⋅𝑉𝐴

(37)

𝑉𝐴= 𝑥𝑃⋅𝐴𝑃𝐴1,2 +𝑉0𝐴1,2

(38)

Here, the piston mass 𝑚𝑃1,2, piston area 𝐴𝑃𝐴1,2 and initial chamber volume 𝑉0𝐴1,2 are taken into

account corresponding to the stage being extended, where subscripts 1 and 2 denote the

cylinder stages. Since the pistons of the telescopic cylinders move vertically, the friction force

𝐹𝑅 includes only the viscous friction force 𝐹𝐷, which is determined according to Equation (25),

while the other frictional forces in Equation (22) are not relevant here.

Pressure losses in the hydraulic circuit are also taken into account and encompass the

pressure losses due to the load-sensing function 𝑝𝐿𝑆, the hydraulic resistances of the pipelines

𝑝𝑙𝑖𝑛𝑒, and the gravity 𝑝𝑝𝑜𝑡. These pressure losses are added to the required pressure at the

𝑄𝐴

𝑚𝐿

𝐹𝐿

𝑥𝑃

󰇗

𝑥𝑃

󰇘

𝐹𝐷

𝐹𝑘

𝑝𝐴

𝐴

𝑃𝐴

𝐴

𝑃𝐴1

𝐴

𝑃𝐴2

𝐴𝑃𝐴1

𝑇

𝑆

respective cylinder actuator 𝑝𝐴/𝐵 to determine the total system pressure at the pump outlet

𝑝𝑃𝑢𝑚𝑝:

𝑝𝑃𝑢𝑚𝑝 = 𝑝𝐴/𝐵+ 𝛥𝑝𝐿𝑆 + 𝛥𝑝𝑙𝑖𝑛𝑒 + 𝛥𝑝𝑝𝑜𝑡

(39)

For the load-sensing pressure loss, the pre-load pressure of the load-sensing valve is

considered, which can be taken from the valve data sheet. The pressure loss in the pipelines

is calculated for a laminar flow according to [Will & Gebhardt, 2008]:

𝛥𝑝𝑙𝑖𝑛𝑒 =𝑄𝐴/𝐵 ⋅64⋅𝜇𝑜𝑖𝑙 ⋅𝑙𝑙𝑖𝑛𝑒

2⋅𝐴𝑙𝑖𝑛𝑒 ⋅𝑑𝑙𝑖𝑛𝑒2

(40)

where 𝜇𝑜𝑖𝑙 is the dynamic viscosity of the hydraulic oil. 𝑙𝑙𝑖𝑛𝑒 is the length, 𝑑𝑙𝑖𝑛𝑒 is the inner

diameter, and 𝐴𝑙𝑖𝑛𝑒 is the cross-sectional area of the pipeline respectively. The pressure loss

due to gravity represents the potential energy of the oil due to its elevation, so that it can be

calculated as:

𝛥𝑝𝑝𝑜𝑡 =ℎ𝑜𝑖𝑙 ⋅𝜌𝑜𝑖𝑙 ⋅𝑔

(41)

where ℎ𝑜𝑖𝑙 is the height to which the oil has to reach and 𝜌𝑜𝑖𝑙 is the density of the hydraulic oil.

In addition, in the functions of the support legs and the front platform tongue, a load-holding

valve is used to prevent the uncontrolled movement of the load. Therefore, the pre-load

pressure of the respective valve is also considered in these functions.

In the pump model, the hydraulic-mechanical map of the pump is used to determine the

absorbed torque 𝑀 based on the calculated system pressure. The power requested by the

motor 𝑃𝑚𝑜𝑡𝑜𝑟 can then be calculated based on the characteristics of the auxiliary gear

connecting the pump and the engine:

𝑃𝑚𝑜𝑡𝑜𝑟 = 𝑀⋅𝑛⋅2𝜋

60⋅𝑖𝑔𝑒𝑎𝑟 ⋅𝜂𝑔𝑒𝑎𝑟

(42)

where 𝑛 denotes the fixed rotational speed of the engine, and 𝑖𝑔𝑒𝑎𝑟 and 𝜂𝑔𝑒𝑎𝑟 are the gear ratio

and efficiency of the connecting gear respectively.

The same models presented in Chapter 3.1 are used here to model the diesel engine and the

electric drive. The total power demand and the total consumed energy of the lifting system for

both trucks, as well as the consumed diesel fuel (for the diesel truck) and the SOC of the

battery (for the electric truck) can then be determined according to Equations (13) to (18). In

addition, the applied output power for the external load to be moved by the cylinder actuator

can be calculated as:

𝑃𝑜𝑢𝑡 =(𝑚𝐿⋅𝑥𝑃

󰇘 +𝐹𝐿)⋅𝑥󰇗𝑃

(43)

and the output energy can be then determined according to Equation (20).

The required data for the implementation of the model equations are taken from the data

sheets of the components involved. The required (but not specified) data can be estimated

from the given information, literature, or assumptions. The overall model is validated by

comparing the resulting simulated pressure and volumetric flow rate curves to those that are

measured whereby the measurement data presented in Figure 2-7 in Chapter 2.3.2 is

employed for this purpose.

Figure 3-12 shows the simulation results for the system pressure and output power of the lifting

system of both trucks for the reference lifting cycle presented in Table 2-3 in Chapter 2.3.2.

Figure 3-12: Simulation results of the power demand of the lifting model

The closeness of the simulation model to the real lifting system can be confirmed by the

correspondence of the simulation results to the measurement data as the pressure curve aligns

with the measurement data. However, the pressure for lifting the container is higher in the

simulation than in the measurement, because a payload of 3,000 kg is assumed in the

simulation, while the measurement was carried out without payload.

As shown in Figure 3-12, a clear difference can be observed in the power curves of both trucks

as the power required in the lifting system of the electric truck is always less than half that of

the diesel truck. Since the hydraulic functions of the container and support legs are identical

on both vehicles, the difference in their output power is related to the different truck drives. In

contrast, the functions of the platform and platform tongue of the electric vehicle are no longer

operated hydraulically but have their own electric drives, which is also reflected in their power

100

150

200

250

100

020 40 60 80 100 120 140 160 180 200 220

Pressure [bar]

Power [kW]

Time [s]

Power of diesel truck Power of electric truck Pressure

demand. It is also to be noted that – despite the low output power required – the power demand

of the support legs function is considered too high. However, this can be traced back to the

additional hydraulic power required for the sub-functions which results in energy losses in the

system. To some extent, this also applies to the front platform and platform tongue in the diesel

truck.

Table 3-4 summarizes the consumption results of the lifting system of both trucks and the

corresponding energy-saving potential. When looking at the individual functions of the lifting

system, it is evident that, overall, the differences are relatively significant, which reflects the

effect of the high efficiency of the electric motor on reducing consumption. This also applies to

the idle time, i.e. the time in which the engine is running without a function being operated.

Table 3-4: Simulation results of the lifting model

Function

Diesel truck

consumption

[Wh]*

Electric truck

consumption

[Wh]

Energy saving

Support legs

158.30

79.20

49.97%

Container

739.20

371.60

49.73%

Front platform

113.10

0.27

99.76%

Front Platform

tongue

40.30

0.27

99.32%

Idling

35.80

14.40

59.78%

Total

1,086.70

465.74

57.14%

*The considered energy equivalence (calorific value) of 1 liter of diesel is 9.79 kWh/L

Approximately 50% of the consumption can be saved in the functions of the container and

support legs whereas, for the front platform and front platform tongue functions, the savings

are much higher as they are close to 100%. This is due to the fact that, as already mentioned,

these functions involve significant pressure losses in the diesel truck, which is avoided in the

electric truck through the use of electromechanical actuators for these two functions. Moreover,

the poor efficiency of the diesel engine with lower power requirements leads to even greater

differences. However, these functions represent a small proportion of the energy requirement

of a complete work cycle and therefore have little effect on the overall saving potential while

the container function has the highest energy requirement, accounting for more than two thirds

of the total consumption in a complete work cycle. This function, therefore, has the greatest

influence on the total consumption efficiency and savings potential.

In general, the results show that the lifting system of the electric truck has a more efficient work

cycle compared to the diesel truck while maintaining a realistic operating performance. The

resulting total energy consumption for a full operating cycle is approximately 1.09 kWh for the

diesel truck and approximately 0.47 kWh for the electric truck which corresponds to a reduction

of 57.14% in the energy consumption of the electric truck compared to the diesel truck. This

means that in the diesel truck, more than twice the energy would have to be supplied to the

engine to achieve the same lifting work cycle.

Figure 3-13 shows the simulated battery SOC of the electric truck for a complete lifting work

cycle. At the end of the cycle, approximately 0.4% of the battery charge has been used up.

Figure 3-13: Simulation results of the battery SOC of the lifting system of the electric truck

89.2

89.4

89.6

89.8

020 40 60 80 100 120 140 160 180 200 220

SOC [%]

Time [s]

4 Efficiency and Potential Analysis

In the following, the energy efficiency of the catering lift trucks considered is evaluated using a

uniform procedure to provide an objective comparison and identify the energy-saving potential.

To this end, the energy efficiency and energy losses of the driving and lifting systems, as well

as the overall efficiency of the entire vehicle, are discussed. The analysis is based on the

simulation results for the energy consumption of both trucks that are presented in Chapters

3.1 and 3.2 according to the operating profile and working conditions defined in Chapter 2.2.

Energy efficiency generally describes the relationship between the benefits that are achieved

and the energy that is used. Considering an energy conversion process in a vehicle, energy

efficiency 𝜂𝐸 is defined as the ratio of the useful output energy 𝐸𝑜𝑢𝑡 to the supplied input energy

𝐸𝑖𝑛 over a certain period of time [Liebl et al., 2014] [Pehnt, 2010]:

𝜂𝐸= 𝐸𝑜𝑢𝑡

𝐸𝑖𝑛 .100%

(44)

Energy loss is the part that is converted into a form of energy that cannot be used again in the

process and it represents the difference between the input energy and the output energy.

In addition, an energy efficiency comparison index Δ𝜂𝐸 is used to enable the comparison

between the different drive concepts of the two trucks being evaluated. This index describes

the percentage increase in the energy efficiency of a new drive 𝜂𝐸𝑛𝑒𝑤 to perform the same work

compared to the energy efficiency of the reference one 𝜂𝐸𝑟𝑒𝑓 and is defined as follows

[Fleczoreck, 2013] [Liebl et al., 2014]:

Δ𝜂𝐸=( 𝜂𝐸𝑛𝑒𝑤

𝜂𝐸𝑟𝑒𝑓 −1).100%

(45)

4.1 Driving System

Figure 4-1 shows the simulation results for both trucks of the TTW energy distribution when

driving. The bars shown represent the variation in useful energy that is released to the wheels

and the losses caused by the driving system for the different driving cycles. The recovered

braking energy in the electric truck is represented by the negative parts.

At first glance, it is easy to recognize that the diesel truck has a significantly higher proportion

of losses than the electrified truck. On the other hand, in the electric truck, in addition to greatly

reducing energy losses thanks to the high efficiency of the electric drive, the energy recovered

during braking may equal or even exceed the energy lost. Since both trucks drove the same

distances, the useful energy (or work performed) of the two trucks is almost the same and the

slight difference in the useful energy is due to the fact that the electrified truck is slightly heavier

because of the additional weight of the battery.

Figure 4-1: Simulation results of the energy distribution of the driving system

To further explain the big differences in energy use between diesel and electric trucks, the flow

diagrams in Figures 4-2 and 4-3 show the distribution of energy efficiencies and energy losses

while driving from the respective energy source in the vehicle to the wheels of both trucks. The

energy efficiency of the driving system is related to the efficiencies of the individual

components responsible for energy storage or conversion. In the case of the diesel truck, the

diesel engine has a very low efficiency for the provision of mechanical energy of around 36.1%.

Accordingly, most of the energy losses, approximately 63.9%, occur in the diesel engine while

a very small part of the energy, around 2.5%, is lost in the transmission and differential.

Consequently, only approximately 33.6% of the energy stored in diesel fuel is ultimately

provided to the wheels.

The driving system of the electrified truck is not only characterized by the higher efficiency of

the electric drive but also by the recovery and storage of braking energy in the battery. The

energy efficiency and loss analysis that is presented thereby represents the offsetting of

provided and circulated energy flows. When providing energy, approximately 10% of the

energy losses are caused by the power electronics including the battery and only 6% of the

energy losses occur at the electric motor while around 5.8% of the energy is lost in the

transmission and differential. Accordingly, approximately 78.2% of the energy supplied by the

battery is delivered to the wheels as useful energy.

-10

Diesel

Truck

Electric

Truck

Diesel

Truck

Electric

Truck

Diesel

Truck

Electric

Truck

Diesel

Truck

Electric

Truck

Cycle 1 Cycle 2 Cycle 3 Total

Energy [kWh]

Useful energy Energy losses Recuperation

Figure 4-2: Efficiency analysis of the driving system of the diesel truck

Figure 4-3: Efficiency analysis of the driving system of the electric truck

In comparison, the average energy losses for the diesel truck are approximately 66.4%

whereas for the electric truck they are around 21.8%. This means that nearly two-thirds of the

wasted energy has been saved. Moreover, roughly 24.8% of the supplied driving energy in the

electric truck goes back into the battery through recuperation. In accordance with the

assumptions in the simulation, the energy demand of the auxiliary units is low and is assumed

to be the same for both trucks and is therefore not taken into account here.

4.2%

1.6%

78.2%

Battery & Power

Electronics η = 90%

Transmission

η = 95%

36.05 kWh

(three measured

driving cycles)

10%

Motor

η = 93.3%

Differential

η = 98%

1.6%

2.2%

2.8%

24.8%

0.6%

Differential

η = 98%

Power Electronics

η = 90%

Transmission

η = 95%

Generator

η = 92.7%

1.8%

0.7%

33.6%

63.9%

Motor

η = 36.1%

Transmission

η = 95%

Differential

η = 98%

79.59 kWh

(three measured

driving cycles)

4.2 Lifting System

The energy distribution of the lifting system based on the simulation results is illustrated in

Figure 4-4, whereby the useful energy delivered to the actuators and the energy losses for

each individual function is shown.

In general, high losses are evident in all functions of the lifting system which leads to a larger

energy demand although only a small amount of energy has to be delivered to the actuators.

As is evident from the figure, the amount of useful energy is mostly too small to be seen on the

bar chart. The function of lifting/lowering the container has the highest energy demand and

accordingly causes the largest part of the energy losses.

Figure 4-4: Simulation results of the energy distribution of the lifting system

The distribution of energy efficiencies and energy losses of the lifting system is shown,

analogously to the driving system, in the flow diagrams in Figures 4-5 and 4-6 for both trucks.

The lifting system of the diesel truck shows poor efficiencies in all functions. In the case of

small functions, in particular, very low efficiencies, i.e. close to zero, are evident due to the

enormous losses compared to the work done. These losses can be attributed to several causes

including the use of the oversized main drive, even for small functions. On the other hand, as

already mentioned, there are fixed losses in the main hydraulic circuit, regardless of which

function is currently in operation. This includes pressure drops due to load-sensing and pump

relief functions, as well as the hydraulic resistance specified in some components.

100

200

300

400

500

600

700

800

900

1,000

1,100

Diesel Truck

Electric Truck

Diesel Truck

Electric Truck

Diesel Truck

Electric Truck

Diesel Truck

Electric Truck

Diesel Truck

Electric Truck

Diesel Truck

Electric Truck

Support legs Container Front

platform

Front

platform

tongue

Idling Total

Energy [Wh]

Useful energy Energy losses

The load-sensing function regulates the system pressure in each section of the lifting work

cycle with the current actuator pressure plus the preset pressure difference, which corresponds

to the setting of the load-sensing valve. While the pump relief function provides the minimum

pressure required by the pump, often with axial piston pumps, to operate properly. This is

usually done by using a preload valve.

For small functions, the fixed losses in the hydraulic circuit are very high compared to the

energy required for the actual work. In addition, a large part of the losses in the function of the

support legs is due to the additional pressure required for the hydraulic load-holding

assignment.

For the electric truck, there are generally fewer losses in the lifting system. Since the functions

of the container and the support legs have the same hydraulic efficiencies as in the diesel

truck, the differences here are only related to the efficiency of the drive whereby a significant

improvement has been achieved in the efficiencies of the functions of the front platform and

front platform tongue thanks to the full electrification of these functions. As can be seen from

the efficiency distribution in Figure 4-6, the losses in the electric drive are small compared to

the losses in the hydraulic circuit, so the overall efficiency is still low for the electric truck lifting

system. In general, the lifting system of the electric truck has a total efficiency of 25.8%, while

that of the diesel truck has a total efficiency of 11.1%.

Figure 4-5: Efficiency analysis of the lifting system of the diesel truck

1,086.7 Wh

(one full

lifting cycle)

59.4%

1.2%

0.1%

Motor

η = 40.6%

16.2%

0.1%

0.5%

11.1%

Front platform

η = 0.26%

Container

η = 41.2%

Front platform tongue

η = 1.3%

0o.ö#-

28.3%

Hydraulic Circuit

(total η = 28.1%)

Gear

η = 97%

Support legs

η = 0.25%

Figure 4-6: Efficiency analysis of the lifting system of the electric truck

4.3 Entire Vehicle

After carrying out the efficiency analysis for each of the driving and lifting systems separately,

the efficiency analysis for the entire vehicle is conducted in the following. The analysis is

carried out here for the performance of three complete work cycles according to the work cycle

diagram of a catering lift truck previously defined in Figure 2-3 in Chapter 2.3, where each work

cycle consists of one of the three measured driving cycles presented in Chapter 2.3.1 and a

full lifting cycle as described in Chapter 2.3.2 and the corresponding simulation results from

Chapters 3.1 and 3.2 are used for this purpose. The overall energy balance of the work

processes for both trucks is shown in Figures 4-7 and 4-8. The total energy requirement for

the three simulated work cycles is 82.85 kWh for the diesel truck. For the electric truck, the

total energy requirement is supposed to be 37.45 kWh, but thanks to recuperation it is reduced

to 28.5 kWh which means that the electric truck only needs around 34% of the energy that the

diesel truck would need. Most of the energy flowing into the process, over 96%, is required for

the driving, and the remaining less than 4% is for the operation of the lifting system. On

average, the diesel truck delivers 32.7% of the total energy consumed in the process while, in

comparison, the electric truck delivers 76.2% on average, while 23.9% of the total energy

consumed in the electric truck is recovered through recuperation.

Motor

η = 89.9%

Gear

η = 97%

Hydraulic Circuit

(total η = 32.9%)

0.2%

32.3%

25.8%

Support legs

η = 0.25%

Container

η = 41.2%

465.74 Wh

(one full

lifting cycle)

Battery and Power

Electronics η = 90%

42.3%

72.1%

Front platform

tongue

8.7/5.1%

39/12.7%

10%

52.7%

2.4%

9.1%

10%

Battery and Power

Electronics η = 90%

Motor

η = 90.3/94.3%

Gear

η = 52/85%

Figure 4-7: Vehicle efficiency analysis of the diesel truck

Figure 4-8: Vehicle efficiency analysis of the electric truck

The energy efficiency of the two trucks and the energy efficiency comparison index are

summarized in Table 4-1 for the driving and lifting systems as well as for the entire vehicle.

Both the driving system and the lifting system of the electric truck have better energy efficiency,

which is more than twice that of the diesel truck. As already mentioned, the lifting system has

very low energy efficiency in both trucks. However, it has no significant influence on the overall

efficiency of the vehicle, since its energy consumption only accounts for a small part of the

total consumption compared to the driving system. For the three complete work cycles, the

energy efficiency of the entire vehicle can be increased from 32.7% to 76.2% through

electrification and further to 100.1% through recuperation, i.e., three times the energy

efficiency of the diesel truck. The energy recovered is greater than the energy losses in the

electric truck, which explains why the overall efficiency of the electric truck is more than 100%

when calculated over the considered work cycles.

82.85 kWh

(3 complete

work cycles)

63.8%

Driving System

32.7%

32.3%

Lifting System

3.5%

0.4%

37.45 kWh

(3 complete

work cycles)

21%

Driving System

76.2%

75.27

Lifting System

2.77%

0.96%

23.9%

6.8%

The calculation of the energy efficiency comparison index shows that the percentage relative

increase in the energy efficiency of the catering lift truck through electrification is 133% and,

when taking recuperation into account, it increases to 206.1%.

Table 4-1: Comparison of the overall energy efficiency of the trucks for the three work cycles

𝜼𝑬 Diesel truck

𝜼𝑬 Electric truck

Δ𝜼𝑬

Driving system

33.6%

78.2%

132.7%

Lifting system

11.1%

25.8%

132.4%

Entire truck

32.7%

76.2%

133.0%

Entire truck

with

recuperation

100.1%

206.1%

However, it should be noted that the results may vary for different applications of the catering

lift truck due to several factors affecting the operating conditions, for example, the

environmental conditions. In addition, ancillary units absorb part of the engine power to perform

auxiliary functions such as cooling, steering, assistance systems, and provision of the onboard

power, which are not considered sufficiently in this analysis. The analysis in this work is carried

out assuming a specific and fixed payload value. However, the effect of payload variance on

the results of the efficiency analysis of the two trucks is discussed in Appendix D.

5 Economic Analysis Based on the Total Cost of

Ownership (TCO)

In addition to the basic technical feasibility, the cost balance for a catering lift truck plays a

major role as the acquisition and operating costs are important decision criteria when choosing

a truck, and the significantly higher acquisition costs of a battery-electric truck compared to a

conventional diesel truck represent the highest hurdle to purchasing at present. On the other

hand, although electric trucks can score with lower operating and maintenance costs, the

question arises as to whether this cost advantage of the electric truck is able to compensate

for its high acquisition costs. This is highly dependent on the usage profile and the service life

of the truck which, to some extent, correlates with the battery life.

A TCO analysis is thus necessary for a fair comparison based on real driving profiles. The

analysis aims to identify economic potential and quantify cost differences for the use of battery-

electric catering lift trucks compared to conventional diesel catering lift trucks under the same

boundary conditions. In this analysis, all of the costs associated with the acquisition and use

of the truck over its entire service life are taken into account as far as possible. In addition,

battery aging, replacement, and residual value are also considered. Possible uncertainties in

the development of influencing factors are depicted in the form of scenarios and sensitivity

analyses.

It should be noted, however, that the results of the TCO do not represent general statements

about the actual profitability of electric catering lift trucks, but rather provide important

information to the operators about the economic potential of these trucks with regard to the

application under consideration.

5.1 Calculation Method

The TCO analysis takes all relevant investment costs over the course of the vehicle ownership

period into account. These are usually divided into fixed costs and variable costs [Hilgers,

2016]. The fixed costs include the costs associated with the purchase of the truck,

infrastructure costs (if any), vehicle tax and insurance, as well as the value at the end of its

useful life (residual value). The variable costs include all costs that depend on the operation of

the vehicle, such as driver costs, fuel or electricity costs, maintenance and repair costs, and

tolls and vignettes. While the cost information can already be available at the time of the

analysis, it can also arise in the future and therefore needs to be predicted.

Both acquisition and operating costs play a major role when considering the TCO of a vehicle

and due to their influence on the total cost of the vehicle they thus determine the buyer's

willingness to purchase. In addition, when comparing the costs of electrified and conventional

vehicles, the policy of state financing of electromobility should be mentioned, as this makes

the economic advantage of electric vehicles even clearer. This also includes environmental

bonuses and tax exemptions for electric vehicles. Driver costs are not usually taken into

account when comparing the TCO for vehicles of the same type, as they are the same

regardless of the powertrain technology and the same applies to any other overhead costs that

are equal for all vehicles being compared.

The TCO analysis in this work is based on the calculation model VDMA-34160 (forecast model

for the life cycle costs of machines and systems). The model represents a generic TCO

calculation procedure that covers all areas of application and thereby ensures comparability.

Figure 5-1 gives an overview of the TCO analysis structure considered in this work in which

the life cycle of the catering lift truck is divided into three phases. In the acquisition phase, the

purchasing costs of the truck and the infrastructure are to be considered. The operation phase

forms the core of the TCO calculation and includes five cost elements while the consumption

costs represent all energy costs for operating the truck (driving and lifting). The costs of

maintenance, vehicle taxes, and vehicle insurance are taken into account as fixed costs that

are independent of mileage. In the recovery phase, the residual value of the truck at the end

of its service life is determined.

Even though they make up a large part of the total costs, the personnel and training costs are

not considered relevant elements in the analysis because these costs are assumed to be the

same for both trucks and thus have no influence on the cost comparison.

Figure 5-1: Composition of the TCO of the catering lift trucks

Since the costs arise at different points in the life cycle of the truck, all TCO elements are

calculated using the present value method whereby the present value references the value of

the future or past costs to the current point in time, assuming a certain interest or inflation rate

respectively [Wagner, 2011].

The present value of the TCO of the truck 𝑇𝐶𝑂 is calculated according to the formula:

𝑇𝐶𝑂 = 𝐴𝐶𝑡0+ 𝐼𝐹𝐶𝑡0+ ∑𝐶𝐶𝑡+ 𝑀𝐶𝑡+ 𝑇𝐶𝑡+𝐼𝐶𝑡

(1+𝑖)𝑡−1

𝑇

𝑡=1 + 𝐵𝑅𝐶𝑡𝑏

(1+𝑖)𝑡𝑏−1 - 𝑅𝑉𝑇

(1+𝑖)𝑇

(46)

where 𝑇 is the service lift duration and 𝑖 is the annual interest rate. 𝐴𝐶𝑡0 and 𝐼𝐹𝐶𝑡0 are

respectively the acquisition costs of the vehicle and the infrastructure for the current year (time

point of calculation) 𝑡0. The future costs over the entire service life of the vehicle are

represented in 𝐶𝐶𝑡 as the energy or fuel costs, 𝑀𝐶𝑡 as the maintenance costs, 𝑇𝐶𝑡 as the

vehicle tax costs and 𝐼𝐶𝑡 as the vehicle insurance costs, each in the future year 𝑡 respectively.

𝐵𝑅𝐶𝑡𝑏 is the battery replacement cost in year 𝑡𝑏 and 𝑅𝑉𝑇 is the vehicle residual value at the

end of its service life.

Since the vehicle operating conditions are the basis of the analysis, the operating scenarios of

the catering lift truck are first defined according to the operating profile and work conditions

presented in Chapter 2.2. At the beginning of each scenario, a complete truck is purchased,

as well as an infrastructure in the case of the electric truck. It is assumed that the purchase

took place at the beginning of 2021. According to the operator information, the service life of a

conventional catering lift truck is 20 years, and it is assumed that the two types of trucks have

the same service life. The TCO analysis will thus extend to the end of 2040 and, accordingly,

the relevant cost elements are calculated over the entire life cycle of both truck types.

In the context of the analysis, the simulation results are used to determine the consumption

costs. While the technical specification and the operating conditions of the vehicle also provide

important data for the analysis, specific data and values are not available for some parameters

and these are therefore estimated based on information from the literature.

Based on the assumed battery lifetime in the respective scenario, a new battery may have to

be purchased during the scenario runtime, the residual value of which is determined at the end

of the scenario.

All estimated future cash flows are discounted to the year 2021 using a specified interest rate.

The cost parameters are determined first, and then the results of the TCO analysis of both

trucks are presented for individual scenarios. The cash values of both trucks are assessed and

compared with each other. Finally, the development of important cost parameters and their

influence on the TCO is examined in the sensitivity analysis. It should be noted, however, that

the TCO analysis presented in this work only relates to catering lift trucks that are used under

the operating conditions outlined here as the costs incurred by the different vehicle models

used under alternative operating conditions can vary widely. In addition, various additional

costs may arise over the entire service life of the trucks. The presented TCO analysis only

includes those costs that represent the most important cost components of the catering lift

truck, and the less significant costs are neglected, whereby these deviations limit the scope of

the analysis results.

5.2 Definition of the Operating Scenarios

To be able to calculate the costs over the life cycle of the catering lift trucks, a general daily

operating scenario for the trucks under consideration has to be defined. According to the

operator information, there is no precise operation plan and hence as soon as a truck is

available, it can be used whereby it is not regulated which truck has to be used or how many

hours a truck has to be in operation per day. Therefore, assumptions have to be made for the

definition of the daily operating scenarios.

To discuss and compare different options, two daily operating scenarios are defined that

represent two possible developments in energy consumption and investment costs for the

entire service life of the considered trucks. In scenario 1, three daily work cycles are assumed,

which represents the average daily work cycles of a catering lift truck whereas scenario 2

assumes five daily work cycles, which corresponds to the maximum daily work cycles of a

catering lift truck. Each work cycle consists of a driving cycle and a lifting cycle. The amount

of energy required to carry out a work cycle is therefore comprised of the energy required to

drive the truck and the energy required to operate the lifting system. The work cycles defined

in Chapters 2.3.1 and 2.3.2 and the corresponding consumption results presented in Chapters

3.1 and 3.2 are used for this purpose. For simplicity, it is assumed that both trucks perform the

same work cycles every day and that the operating conditions do not change over the life cycle

of the trucks. Only one week per year is excluded, which is intended for maintenance.

Since the cost of the battery is of central importance for the profitability of electric vehicles, the

probability of battery replacement is also discussed in the operating scenarios. As the service

life of the battery largely depends on the charging behavior [Doppelbauer, 2020], battery wear

and tear during routine operation should be taken into account. An important principle that

delays battery aging is that it should be charged regularly, and deep discharge should be

avoided as much as possible. This reduces the electrochemical load on the battery and

increases its service life. Battery discharging is described by the depth of discharge (DoD),

which represents the percentage of the battery that has been discharged relative to its total

capacity. Fast charging also plays a role in the aging of the battery, because charging with

high currents puts a special strain on the battery cells. If a battery system is not optimally

designed for this, rapid charging can lead to local overheating, which accelerates the

degeneration process of the battery.

In the electric truck, a lithium-ion battery with a total capacity of 113 kWh is used as the

accumulator. According to the battery manufacturer, the number of charging cycles over the

entire service life of the battery is 4,800 at a DoD of 80%. Assuming an average DoD of 80%

in the electric catering lift truck, 90.4 kWh is available for a fully charged battery each time.

According to the simulation results, the electric truck consumes 204,060 kWh in scenario 1

and 296,424 kWh in scenario 2 over its entire service life. This means that the battery needs

to be charged 2,257 times in scenario 1 and 3,279 times in scenario 2 which is significantly

lower than the available battery charging cycles. Accordingly, it is (theoretically speaking) not

necessary to change the battery during the service life of the truck in both scenarios. However,

while in scenario 1 it is assumed that the battery is charged during the longer night break with

a lower charging power to protect the battery, in scenario 2 on the other hand, it is assumed

that the battery is not being charged regularly but rather is exposed to poor charging conditions.

Subsequently, rapid charging and extreme charging, i.e., full charging (DoD = 100%) and deep

discharge (DoD = 0%) approaches are assumed to take place in scenario 2, which accelerates

the aging of the battery. Based on this, a battery replacement is assumed in the fifteenth year

of the truck service life in scenario 2.

The key data for the resulting daily operating scenarios are summarized in Table 5-1.

Table 5-1: Key data of the defined daily operating scenarios of the catering lift trucks

examined

Parameter

Scenario 1

Scenario 2

Work cycles

Traveled distance [km]

27.16

38.64

Operating hours [h]

1:26

2:06

Diesel truck consumption [kWh]

82.85

(8.46 liter diesel)

121.69

(12.43 liter diesel)

Electric truck consumption [kWh]

28.5

41.4

Battery replacement

Fifteenth year

5.3 Important Influencing Factors on the TCO Analysis

Since the TCO analysis extends over the entire service life of a catering lift truck, the total cost

is influenced by the variables that change over time. The analysis, therefore, focuses on the

period from 2021 to 2040 and depicts possible uncertainties in the development of influencing

factors during this period. The following factors are important for the cost development over

the calculation period: the economic interest and inflation rates and the price development of

diesel, electricity, and batteries. In the following, these factors are discussed, and assumptions

are made based on the relevant literature regarding their development for the time period

considered.

5.3.1 Interest and Inflation Rates

During the period of the vehicle service life, the costs are incurred at different times, and hence,

to make the costs as precisely comparable as possible, the different timescales of the costs

must be taken into account. According to the definition in the Gabler Business Lexicon, the

interest and inflation rates fulfill the time compensation function, with payments occurring at

different points in time being discounted or compounded to a reference point in time [Alisch et

al., 2004]. The time of commissioning of an investment property is normally chosen as the

reference point. By means of discounting, the values of future payments that are made after

the reference point are calculated back using the interest rate to a point in time that precedes

the payments. This means that discounting shows how much an amount of money was worth

at an earlier point in time. In contrast, all payments that occur before the reference point are

compounded using an inflation rate. The compounding thus determines how much an amount

of money will be worth at a later point in time [Wöhe et al., 2016].

The interest rate for companies is determined by the required minimum interest rate and the

entrepreneurial risk premium. The required minimum interest rate is derived from the base rate

which is the interest rate issued by the German Federal Ministry of Finance

(Bundesministerium der Finanzen BMF) for a risk-free return whereby the entrepreneurial risk

varies depending on the company. Since no specific data about the operating company of the

catering lift truck is available, a constant annual interest rate of 3% is assumed for all calculated

costs after the reference point. For costs prior to the calculation period, an average annual

inflation rate of 1.23% was assumed, which corresponds to the average inflation from 2018 to

2020 [Statista, 2022a].

5.3.2 Price Development of Diesel

The prices for conventional fuels in Germany mainly relate to the import of crude oil which is

significantly influenced by world market prices and exchange rates [Ederington et al., 2019].

Taxes and duties also play an important role in determining fuel prices. In Germany, energy

tax and value-added tax (VAT) as well as the CO2 tax make up a large proportion of the price

of petrol and diesel [Schlesinger et al., 2014] and, as a result, the price of diesel fuel in

Germany is subject to strong fluctuations [Statista, 2022b].

Since the beginning of 2021, CO2 emissions in individual sectors, including the energy sector,

have been subject to a CO2 price of €25 per ton, which is set to increase to €55 per ton by

2025 as was decided by the grand coalition in the Climate Protection Act 2019 [BReg, 2019].

Hence, many experts and research institutes expect a further surcharge on the fuel price for

the end customer in the coming years.

A reference forecast for the development of energy prices was presented in 2014 by the

consulting company Prognos AG as a likely development of the energy industry up to 2050.

The forecast assumes a relatively moderate increase in the diesel price since the proportion

of taxes and duties in the fuel price is assumed to be constant in real terms. Accordantly, a

linear development of fuel prices up to 2050 was assumed. Based on this forecast, the prices

for petrol and diesel fuel will be approximately 25% higher in 2030, and around 40% higher in

2050 in comparison to the prices from 2011 [Schlesinger et al., 2014].

A more recent study commissioned by the Federal Ministry for Economic Affairs and Climate

Action (Bundesministerium für Wirtschaft und Klimaschutz BMWK) is presented in 2020. In

this study, projections and impact assessments are made for the further development of the

energy system up to 2050. Based on the findings, it is assumed that after 2026 the CO2 tax

will rise sharply and therefore the price of fuel will increase even further [Kemmler et al., 2020].

Likewise, in the estimates according to other forecasts from the literature, such as [IEA, 2020b]

and [Capros et al., 2016] a significant increase in fuel prices is to be expected in the medium

term for all energy sources, among other things for reasons of climate protection. In summary,

it can be said that the anticipated CO2 tax will affect the price of diesel over the next 20 years,

despite the expected decline in demand for fossil fuel.

The assumptions for the development of fuel prices in this work are concluded from the

mentioned forecasts as well as further scientific publications. Figure 5-2 shows the

development of diesel prices, both with and without VAT, which is considered in the TCO

analysis. The diesel price is assumed to rise significantly by 2030 whereas, from 2030 onward,

it will only increase moderately with a constant increase per year. Since the areas of application

considered are commercial use of the catering lift truck, the value of VAT was excluded from

the diesel prices.

Figure 5-2: Estimated development of diesel prices in Germany over time (Source: own

assumptions based on literature review)

5.3.3 Price Development of Electricity

The electricity price in Germany is made up of three main components. The first component

comprises the costs for the generation and distribution of electricity which also includes the

provider's share of the profit, which can vary depending on the electricity provider. The second

cost component concerns the fees for the network use while the last component consists of

various surcharges and taxes. The main part is related to the surcharge for financing the

expansion of renewable energy. According to the German Association of Energy and Water

Management (Bundesverband der Energie- und Wasserwirtschaft BDEW), in 2020 surcharges

and taxes comprised 52% of the electricity price, whereas generation and distribution

contributed 23% and network fees added 25% to the overall costs [BDEW, 2021].

The Renewable Energy Act (Erneuerbare-Energien-Gesetz EEG) presented by the German

Federal Government, is intended to enable the long-term sustainable development of a secure,

economical, environmentally and climate-friendly energy supply. With this act, the Federal

Government has set itself the goal of increasing the proportion of electricity generated from

renewable energies to 65% by 2030. Furthermore, before the year 2050, all electricity that is

generated or consumed in Germany should be generated in a GHG-neutral manner [BMJ,

2020b]. Achieving these goals requires far-reaching changes in the infrastructure and the

expansion of the power grid for renewable energy generation. The investment costs are later

0.4

0.6

0.8

1.2

1.4

1.6

1.8

2.2

Diesel price [€/L]

With VAT Without VAT

passed on to the end consumer, which leads to an increase in the price of electricity. This is in

addition to the national pricing of CO2 emissions that was mentioned previously, which will also

be reflected in additional costs for end consumers.

The development of electricity prices is characterized by a continuous increase over the years

despite the low prices for primary energies and network charges. This is attributed to the

increasing EEG levy. Although a slight reduction in the EEG levy in autumn 2014 initially

lowered the electricity price in 2015, it subsequently rose again after that.

There are many forecasts and studies with different results on the price development of

electricity for the next 20 years. These can be traced back to different basic assumptions

related, on the one hand, to the expected development of the CO2 or the EEG levies, and on

the other hand, to the evolution of fossil fuel prices in the coming period. Moreover, other

studies take the effect of the growing electricity demand on the final electricity prices into

account.

To make assumptions about the electricity prices to be used in the TCO analysis, a survey

was made of the forecasts for the electricity price development that is available in the literature

whereby the assumptions of the reference forecast in [Schlesinger et al., 2014] were mainly

considered. Furthermore, the findings from a study provided by the German Energy Agency

GmbH, which discusses different scenarios for the transformation paths of the energy system

in Germany up to 2050 were also taken into account [Bründlinger et al., 2018].

In general, the studies indicate that electricity prices are expected to rise in the medium term

as a result of subsidizing green electricity in addition to the increase in fuel and CO2 taxes.

However, the question arises as to whether electricity prices will also continue to increase in

the long term or whether they could ultimately even fall as a result of the energy transition. The

estimates assume that electricity prices will fall when the nuclear phase-out is complete and

the electricity generation increases as a result of the expansion of renewable energies, which

means that most subsidies will end.

When it comes to electricity prices, however, a fundamental distinction must be made between

households and industry as industrial electricity prices are relevant for companies that

consume more than 100 MWh of electricity per year. Depending on the amount consumed,

commercial customers can receive lower tariffs due to numerous exceptions [BDEW, 2021]

[Schlesinger et al., 2014]. The electricity price development considered in this work for the

period up to 2040 is shown in Figure 5-3, which represents subjective assumptions based on

projections from the cited literature and further research.

For the catering companies that are the users of the catering lift trucks considered in this work,

there is the possibility of obtaining an industrial tariff as an electrical consumption of this

magnitude turns out to be realistic for a fleet of electric catering lift trucks. Based on the

simulation results, and as presented in Table 5-1, an average daily operating scenario requires

approximately 28.5 kWh of electrical energy, and hence using the truck on 358 days per year

results in about 10.2 MWh of energy consumption. According to the operator company, a fleet

of catering lift trucks comprises about 12 trucks which would require a total electricity

consumption of approximately 122.4 MWh per year. Therefore, for the purpose of the TCO

calculation, industrial electricity prices are taken into account.

Figure 5-3: Estimated development of electricity prices in Germany over time (Source: own

assumptions based on literature review)

5.3.4 Price Development of Battery

The economics of a battery electric truck, especially in the larger truck classes, mainly depends

on the cost of its battery and the further development of battery prices is therefore of central

importance for the profitability of these trucks. Looking at the prices on the international market,

it is noted that in recent years there has been a significant decline in battery prices. However,

future price developments are still subject to great uncertainty and numerous forecasts for

battery price development have been issued by various associations, research institutes, and

consulting firms. In some cases, although they deviate quite markedly from one another, the

common denominator of the forecasts is that they all assume that battery prices will continue

to fall in the near future.

In [Nykvist & Nilsson, 2015], a summary of future cost estimates is presented showing various

degrees of reductions in future prices of lithium-ion batteries for electric vehicles. In other

publications such as [Schmidt et al., 2017] and [Fluri, 2020], a larger number of battery

technologies for electric vehicles are analyzed and price developments are estimated based

on the market size. According to the latest forecasts by the research company BloombergNEF,

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Electricity price [€/kWh]

Industry price Household price

in the 2020 Battery Price Survey, average battery prices will be around 100 USD/kWh by 2023

and, from this point on, electric vehicles could be produced for the mass market at the same

price as vehicles with internal combustion engines [BloombergNEF, 2020].

However, the prices of lithium-ion batteries are dropping much faster than predicted in the

forecasts. On the one hand, this price drop is attributed to the increasing order size and

increasing electric vehicle sales and, on the other hand, to the introduction of new pack designs

and the increasing diversification in the chemicals used in battery manufacturing processes

[König et al., 2021] [Fries et al., 2017] [Goldie-Scot, 2019].

However, the development of lithium-ion batteries for heavy-duty vehicles has not yet made

great progress as heavy-duty vehicles require batteries of higher performance and longer

lifetimes than batteries for passenger cars. This is compounded by the small number of units

that the development costs have to be allocated to in this segment, which results in significantly

higher prices compared to batteries for passenger cars.

Therefore, the estimated battery prices in forecasts cannot directly be used for the application

under consideration. However, the general trend in battery price development is tracked to

create a conservative projection of the future cost development for the battery used in the

catering lift truck. The forecast shown in Figure 5-4 thus represents subjective assumptions

made on the basis of a comprehensive comparison of various cost forecasts from the literature,

whereby a decline of 50% by 2030, 60% by 2035, and 70% by 2040 relative to the current

value of the battery used in the catering lift truck is assumed.

Figure 5-4: Estimated development of battery prices in Germany over time (Source: own

assumptions based on literature review)

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Relative Battery Price Developement

5.4 Cost Elements

In the following, the cost elements of the TCO analysis, which are illustrated in Figure 5-1, are

determined based on the previously defined operating scenarios for the catering lift trucks

under consideration whereby the procedure for deriving the cost elements is explained and the

underlying sources are named.

5.4.1 Vehicle Acquisition Costs

Fleet vehicles are usually leased or purchased, whereby leasing involves less effort to resell

and allows easier access to newer vehicles, making it particularly suitable for vehicles that

have a short service life. Vehicles that are to be used for a longer period are mainly purchased

and the purchase costs are therefore considered for the catering lift truck application. The

purchase cost plays a major role in the assessment of whether the use of an electric truck is

more efficient than a diesel truck since it accounts for the largest portion of the total costs. The

purchase costs for electric trucks are still very high and, in general, the expensive battery

makes the purchase price of electric trucks significantly higher than that of conventional trucks.

At present, the purchase costs of a battery powered heavy-duty truck are two to three times

that of an equivalent diesel-powered heavy-duty truck [Mareev, 20018] [Jöhrens et al., 2021].

The cost of purchasing a truck is made up of the basic costs for the vehicle body, the costs of

power train components and other costs depending on the drive technology, such as the

traction battery for electric trucks. In the case of a catering lift truck, the purchase cost is

composed of the costs of the basic truck including the power train, and the costs for additional

components making up the desired truck configuration, the most important of which is the

cooling in the container. Some ancillary service costs are incurred later such as the transport

cost to the user or approval for commercial road haulage.

According to the manufacturer and operator information, the conventional diesel catering lift

truck, with the characteristics listed in Table A-1 in Appendix A, was purchased in 2018 for

approximately €120,000 which corresponds to the price for a complete vehicle with a typical

configuration. In contrast, the cost of the newly developed electric catering lift truck, the

characteristics of which are also listed in Table A-1 in Appendix A, in the same configuration,

including the battery, amounted to €280,000 [Schmälzle, 2018]. These prices are considered

for the TCO analysis after being compounded to the year 2021, which is the reference time

point for the analysis. However, the truck delivery and registration costs are not taken into

account here as they did not fall within the project period.

According to the announcement by the German Federal Ministry for Digital and Transport

(Bundesministerium für Digitales und Verkehr BMDV), start-up financing was set to cover 80%

of the additional investment costs for a climate-friendly commercial vehicle compared to a

conventional diesel vehicle. In addition, the charging infrastructure required for operating

electric commercial vehicles is also funded to 80% of the eligible costs [BMDV, 2021].

Accordingly, the option of financing 80% of the additional costs of the electric catering lift truck

compared to the diesel truck will also be considered in the calculation.

5.4.2 Infrastructure Acquisition Costs

The switch to a new drive technology usually incurs additional costs for the infrastructure for

the energy supply of the respective vehicles. Therefore, in addition to the vehicle costs, the

costs for the acquisition of charging infrastructure for the electric catering lift truck are also

taken into account while there are no infrastructure costs for the diesel catering lift truck, as

there is always a diesel filling station available nearby. It is therefore assumed that the

infrastructure costs for the diesel filling station are already included in the fuel costs [Mareev,

2018].

The charging infrastructure costs are largely dependent on the specific conditions of the

application, such as the number of vehicles in the fleet, the operating profile, and the charging

concept. In addition to the purchase costs, there are other costs for the setup of the charging

station, including, for example, installation costs and costs of charging cables and assembly.

In addition, further costs for setting up a network connection may arise. These can also vary

greatly depending on the location and size of the electric vehicle fleet or the number of charging

stations [Hall & Lutsey, 2019].

Since there is no detailed information about the cost parameters of a charging station for the

electric catering truck, assumptions are made based on information from the operator. When

the electric prototype of the catering lift truck was purchased in 2018, a compatible charging

station of 80 kW was purchased for €25,000 [Schmälzle, 2018]. This price is considered for

the TCO analysis after being compounded to the year 2021 whereby it is assumed that the

setup costs are also included in the purchase price. The network connection costs are

irrelevant to the application while the option of funding with a financing rate of 80% for the

infrastructure is also considered. Since the service life of the charging stations usually exceeds

that of the vehicles, no replacement is considered in the analysis.

5.4.3 Fuel or Energy Costs

The fuel or energy costs are determined based on the consumption results of the simulation

model and the respective fuel or electricity prices whereby the cost development depends on

the operating scenarios defined in Chapter 5.2. First, the annual vehicle consumption is

calculated from the daily consumption given in Table 5-1 for the respective operating scenario.

As previously mentioned, the catering lift truck is expected to be operated every day, except

for just one week per year during which maintenance and repair work on the truck is carried

out. The consumption costs per year are thus calculated from the total annual consumption

and the corresponding predicted diesel or electricity price in the respective year. The assumed

diesel and electricity price forecasts presented in Chapters 5.3.2 and 5.3.3 are taken into

account here. Since the costs are incurred in different years, they are converted to their present

values using the interest rate. The current values are finally summed up for the years 2021 to

2040 to obtain the consumption costs over the entire service life and the costs are determined

separately for each of the operating scenarios considered.

5.4.4 Maintenance Costs

Electric vehicles have proven to be more advantageous than conventional vehicles in terms of

maintenance costs [Den Boer et al., 2013] [Mareev, 2018]. Since electric vehicles have a

simpler structure, the proportion of wearing parts they contain is greatly reduced compared to

that found in conventional vehicles. In addition, the wear and tear on brake systems are

reduced due to recuperation, thereby increasing the service life of the electric vehicle

components which are thus replaced less frequently than the parts in conventional vehicles.

In addition, some maintenance work that is required for vehicles with internal combustion

engines, such as oil changes and emissions tests, is no longer necessary with electric vehicles.

Therefore, determining the maintenance costs plays an important role in the TCO analysis. In

view of the early market stage and relatively small series production vehicles, there are no

comprehensive empirically proven findings regarding the maintenance of electric trucks.

Therefore, most of the values available in this regard are estimates. In some literature such as

the work of [Den Boer et al., 2013] and [Kleiner & Friedrich, 2017], it is estimated that the

maintenance costs for electric trucks are approximately one-third lower than for their internal

combustion engine counterparts. Consequently, this is considered in the context of the TCO

analysis in this work.

According to the operator, a fixed amount is paid per year for the maintenance and inspection

of a conventional catering lift truck, and every truck is checked once a year. As this is a special

vehicle, the inspection is carried out in two steps. In the first step, it is checked like a regular

truck according to technical regulations while in the second step, a more specific check is made

according to the special requirements of a catering lift truck. The entire maintenance and

inspection work takes one week and is carried out by a workshop at Frankfurt Airport. For the

electric truck, an overall reduction in maintenance costs of 33% compared to conventional

comparison vehicles is assumed. However, additional annual costs are assumed for the

maintenance of the charging infrastructure and, in this case, 2% of the acquisition costs of the

charging station are applied. The maintenance costs are considered to include all repair costs

to be incurred and are assumed to be constant in real terms over time.

5.4.5 Vehicle Tax Costs

In Germany, an annual motor vehicle tax is to be paid by the vehicle owner. This is determined

according to the Motor Vehicle Tax Act of the Federal Ministry of Justice (Bundesministerium

der Justiz BMJ) depending on the vehicle's gross weight and emission class [BMJ, 2020a].

Battery electric vehicles are tax-exempt for the first 10 years after registration and, after this,

the annual tax depends on the total weight of the vehicle. The annual tax for the catering lift

truck is calculated using the vehicle tax calculator of the BMF [BMF, 2017]. For a diesel truck

with a permissible total weight of 21 tons and emission class G1, the annual tax is €1,425

whereas, for an electric truck of the same weight, the annual tax is only €278. This applies

from the eleventh year of the service life of the electric truck.

5.4.6 Vehicle Insurance Costs

The insurance costs are only considered for the contribution to motor vehicle liability insurance

and thus optional comprehensive insurance is not taken into account for the calculation. Since

the insurance costs are generally independent of the drive train technology, they are set

equally for both conventional and electric trucks. The insurance costs for the catering lift truck

are calculated using the fleet premium calculator of an insurance broker portal [Georg Soller,

2021] whereby insurance premiums for trucks with an engine power of up to 149 kW are taken

into account. The insurance premiums are assumed to be constant in real terms over time for

the period of the TCO analysis.

5.4.7 Battery Replacement Costs

Assuming that lithium-ion batteries will remain dominant in the automotive sector in the future

[Thielmann et al., 2017], in the case of a battery replacement, it is assumed that a similar

battery will be purchased in the given year while taking the development of battery costs into

account. The service life of the battery used in the electric catering lift truck is defined according

to two different scenarios as already presented in Chapter 5.2. It is assumed that the battery

in scenario 1 does not have to be changed, while in scenario 2 it should be changed once in

the fifteenth year of the truck's service life, i.e. in 2035. The battery replacement costs are

determined based on the battery's current price and forecast assumptions of the battery price

development. The manufacturer indicates that the current costs for a LiFePO4 battery with a

total usable energy of 113 kWh amount to €110,000, which is to be understood as a pack price

that represents the end customer price. According to the assumed forecast in Chapter 5.3.4,

the price of the battery will tend to decrease by 60% of the current value in 2035 and the

estimated battery replacement cost is calculated accordingly, taking the interest rate into

account.

5.4.8 Residual Value

At the end of their service life, vehicles are typically either resold at residual value or scrapped.

The residual value of a vehicle depends on several factors, most notably the type of engine,

mileage, and how long the vehicle has been in use [Hacker et al., 2011]. Heavy-duty trucks

are often used for a long time, and thus the residual value at the end of their service life is

relatively low. Due to their early market stage, the residual value of electric heavy-duty trucks

in particular is subject to great uncertainty. Since no specific information is available on the

depreciation of the catering lift trucks, it is assumed that the trucks will no longer be usable at

the end of their long service life. Consequently, no potential vehicle residual value is included

in the TCO analysis.

In the case of the electric catering lift truck, however, the residual value of the batteries is

considered. It is assumed that the battery can be resold at its residual value at the end of the

service life of the truck, which is determined based on the remaining charging cycles and the

current price of the battery. In scenario 1, the battery still has more than half of its charging

cycles at the end of the truck service life whereas, in scenario 2, the replaced battery still has

more than two-thirds of its charging cycles. Taking into account the depreciation in value due

to the fact that the battery is no longer new, it is assumed that the battery still has a residual

value of half the price in scenario 1 and two-thirds of the price in scenario 2. The residual

battery value is calculated based on these assumptions taking into account the battery price

development in Chapter 5.3.4 and the interest rate. No residual value is taken into account for

the charging infrastructure in the TCO analysis.

5.5 Comparison of the TCO of Conventional and Electrified

Catering Lift Trucks

Table 5-2 summarizes the cost parameters of both catering lift trucks under consideration,

which have been calculated as explained in the Chapter 5.4. These are used to calculate and

compare the life cycle costs for the trucks for the period from 2021 until 2040 for the defined

operating scenarios in the Chapter 5.2. The calculation is carried out according to the Equation

(46) and taking into account the interest rate and the price development of the diesel, electricity

and battery presented in the Chapter 5.3. Potential cost differences between the two trucks

that arise on the basis of the TCO comparison are then discussed in order to understand when

a new technology can become cost-effective, while in the next section a more detailed

sensitivity analysis is presented comparing the influence of uncertainties in different TCO cost

parameters.

This TCO analysis attempts to account for all possible costs involved in using a catering truck,

gathered through inquiry to the truck manufacturer and truck operator and further research,

however additional costs could arise for certain individual applications of the truck.

It should also be noted that the costs considered here are based on current calculations,

however, all cost parameters are subject to change with time. Therefore, the analysis

represents the basis for an overall cost evaluation specific to the catering lift truck application

and reflects the current state of the truck electrification.

Table 5-2: Cost parameters of the TCO analysis

Parameter

Diesel Truck

Electric Truck

Source

Vehicle purchasing price

€124,483

€290,460

[Schmälzle, 2018]

Infrastructure purchasing price

–

€25,934

[Schmälzle, 2018]

Potential funding

–

€153,529

[BMDV, 2021]

Annual consumption: Scenario 1

3,030 L/a

10,203 kWh/a

Simulation results

Annual consumption: Scenario 2

4,450 L/a

14,820 kWh/a

Simulation results

Annual maintenance costs

€7,000/a

€5,209/a

(including

infrastructure)

LSG Sky Chefs,

[Den Boer et al., 2013],

[Kleiner & Friedrich, 2017]

and own assumptions

Annual vehicle tax costs

€1,425/a

€278/a (from

eleventh year)

[BMF, 2017]

Annual vehicle insurance costs

€1,121/a

[Georg Soller, 2021]

Battery replacement costs

(scenario 2 in 2035)

–

€29,089

Truck manufacturer Terberg

and own assumptions

Residual value: Scenario 1

–

€9,410

Truck manufacturer Terberg

and own assumptions

Residual value: Scenario 2

–

€12,546

Truck manufacturer Terberg

and own assumptions

Figure 5-5 presents the results of the TCO analysis in which the vehicle total costs are broken

down into their components. It should be noted that all costs represent current values, which

means that inflation-adjusted or interest-adjusted values based on the reference year 2021 are

taken into account rather than nominal values.

Figure 5-5: Results of the TCO analysis for the defined operating scenarios

In general, the results of the TCO analysis for the catering lift truck in Figure 5-5 show high

additional acquisition costs, but on the other hand also show significant cost advantages for

the use of the battery-electric variants compared to the conventional diesel variants and a cost

difference in favor of the electric truck in the recovery.

Vehicle costs make up the largest proportion of the total costs. As an established technology,

the conventional diesel catering lift truck causes significantly lower vehicle costs, which

account for 34–37% of the total costs. In contrast, the vehicle costs of the electric truck are

more than double those of the diesel truck and they account for at least 60–67% of the total

costs. This is mainly due to the high costs of the large battery pack and, although the costs for

the charging infrastructure also represent an additional cost component for the electric truck,

these are low compared to the vehicle costs.

-€50,000

€50,000

€150,000

€250,000

€350,000

€450,000

€550,000

Diesel Truck Electric

Truck

Electric

Truck with

Funding

Diesel Truck Electric

Truck

Electric

Truck with

Funding

Scenario 1 Scenario 2

TCO [€]

Vehicle Infrastructure Fuel/Energy Maintenance

Vehicle Tax Vehicle Insurance Battery Replacement Risidual Value

The proportion of maintenance costs is also relatively high, and it makes up the second largest

share. Although the maintenance costs of the electric truck are lower than that of the diesel

truck, they still account for a large proportion of the total costs.

The fuel or energy costs account for the third-largest share of the total costs in both operating

scenarios whereas the vehicle tax and insurance costs only account for small proportions of

the total costs of both trucks. For the electric catering lift truck in particular, the tax costs have

very little impact on the overall costs which is due to the 10-year tax exemption for electric

vehicles and the very low taxation of electric vehicles in general, which makes a noticeable

difference compared to the tax costs for the diesel truck. In contrast, the insurance costs do

not cause any difference in the total costs as they are equal for both trucks.

In scenario 1, the battery has a long service life and therefore there are no additional costs for

changing the battery. This reduces the total costs in this scenario compared to scenario 2, in

which the costs for a battery replacement are added in the fifteenth year of the service life of

the electric truck.

The trucks have no residual value after the end of their service life because both scenarios are

based on the full useful life of a catering lift truck. For the electric vehicle, however, the battery

still has a residual value at the end of the truck's service life in both scenarios, which contributes

to reducing the difference in total cost between the two trucks.

In the following, the cost comparison is first made for the operation phase only to more clearly

illustrate the advantages of using the electric truck. After this, the acquisition and recovery

phases are also taken into account in the comparison, and, accordingly, a conclusion is drawn

concerning the range in which the acquisition costs of the electric truck should lie to be more

advantageous than conventional trucks.

Figure 5-6 shows the development of the payouts in the operation phase of both trucks for the

two different scenarios. These include energy costs, maintenance costs, vehicle taxes, vehicle

insurance costs, and battery replacement costs. Figure 5-7 portrays the development of the

present values of these payouts, which reflects the effect of the interest rate.

From the data presented, it is evident that the payout curves show slightly positive slopes due

to the increasing annual consumption costs of both diesel and electric trucks. This is due to

the expected increase in fuel and electricity prices until 2030. From the year 2030 onwards, it

is assumed that electricity prices will decrease while fuel prices continue increasing. The slight

increase in the costs of the electric truck from 2030 to 2040 is due to the tax costs after the

tenth year while the reason for the sudden increase in the operation costs of the electric truck

in 2035 is the cost of the replacement battery.

Figure 5-6: Development of the operation phase payouts over time

In contrast to the payout curves, the present value curves of both trucks show falling slopes

due to the effect of the interest rate whereby all payments are discounted to the reference time

for the purchase of the trucks. The further away from the reference time the payment is made,

the more it is discounted. The effect of interest rate is most clearly seen in the value of battery

replacement in the electric truck in scenario 2, as this represents the highest value over the

course of the operation phase.

The comparison shows that the electric catering lift truck is more advantageous than the diesel

truck in the operation phase under the examined conditions which means that these trucks can

already be operated economically today compared to the corresponding conventional trucks,

as there is a major advantage in terms of operating costs.

5,000

10,000

15,000

20,000

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Costs [€]

Scenario 1

Diesel Truck Electric Truck

10,000

20,000

30,000

40,000

50,000

60,000

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Costs [€]

Scenario 2

Diesel Truck Electric Truck

Figure 5-7: Development of the operation phase present values over time

However, despite these findings, the assessment of whether the transition to a battery-electric

catering lift truck is efficient greatly depends on the acquisition costs of the truck, since they

make up the largest proportion of the total costs. The acquisition costs of the electric catering

lift truck are currently too high and although the possibility of government funding or subsidies

for electric vehicles is often discussed, at the moment there are no concrete decisions in this

regard. To take the different options into account, the costs of the two trucks are compared for

various possible financing scenarios based on different assumptions regarding the acquisition

costs.

Basic TCO comparison without funding: The costs of the two trucks are initially compared

without taken any assumptions into account. The present values of the cost differences

between the two trucks are listed in Table 5-3 according to the phases of the TCO analysis.

5,000

10,000

15,000

20,000

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Costs [€]

Scenario 1

Diesel Truck Electric Truck

10,000

20,000

30,000

40,000

50,000

60,000

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Costs [€]

Scenario 2

Diesel Truck Electric Truck

Table 5-3: Basic comparison of the TCO of the two trucks

Scenario 1

Scenario 2

Acquisition Phase

€191,911

Operation Phase

– €80,128

– €66,917

Recovery Phase

– €9,410 €

– €12,546

Total

€102,373

€112,448

The comparison shows that the acquisition costs of the electric truck are much higher than

those of the diesel truck resulting in a cost difference of €191,911 in the acquisition phase,

which means an increase of 154%. This leads to the fact that the acquisition of an electric

catering lift truck, including the charging infrastructure, is nearly 2.5 times more expensive than

a conventional diesel truck. On the other hand, based on the consumption scenarios and the

fuel and electricity prices assumed in the analysis, the electric truck achieves a cost advantage

in the operation phase of €80,128 or €66,917, depending on the operating scenario, over the

entire life cycle of the truck. This means that the operating costs of the electric truck are 29-

40% lower than those of the diesel truck. In scenario 2, there are additional costs for a

replacement battery for the electric truck, but these are more than compensated in the course

of use by significant savings in the operating costs, in particular the energy costs. The cost

difference in the recovery phase between the two trucks corresponds to the rest value of the

battery in the electric truck, which is calculated as indicated in Chapter 5.4.8. The batteries

used for the electric truck have a residual value of €9,410 scenario 1 or €12,546 scenario 2 at

the end of the truck's service life.

As a result, the incremental costs of the acquisition phase could not be offset by the cost

savings in the operation and recovery phases. The total additional costs of the electric truck

over the entire life cycle are €102,373 in scenario 1 and €112,448 in scenario 2. This makes

the TCO of the electric truck around 30% higher than that of the conventional diesel truck in

both scenarios. Given that the current prices for electric trucks are relatively high, the

electrification of the catering lift truck is currently not economically feasible without funding and

the purchase price would either have to drop sharply or be lowered through subsidies to give

the electric truck an advantage.

TCO comparison with funding: If the funding of 80% of the additional costs of the electric

catering lift truck and the corresponding charging infrastructure is calculated in the specific

case, this amounts to €153,529 which would have a very large impact on the total costs of the

life cycle. The present values of the cost differences, in this case, are listed in Table 5-4.

Table 5-4: Comparison of the TCO of the two trucks when government subsidies are

considered

Scenario 1

Scenario 2

Acquisition Phase

€38,382

Operation Phase

– €80,128

– €66,917

Recovery Phase

– €9,410 €

– €12,546

Total

– €51,156

– €41,081

Under this condition, the additional acquisition costs of the electric truck, which are now only

€38,382, could be compensated by the cost savings in the operation phase. Moreover, the

electric truck would have an overall cost advantage of €51,156 in scenario 1 or €41,081 in

scenario 2 at the end of its service life. This makes the TCO of the electric truck 11–15% lower

than that of the conventional diesel truck and, in this case, the use of the electric truck would

be efficient for the end user.

TCO Comparison based on further assumptions regarding battery capacity and

charging station:

Since the catering lift truck is only used to deliver catering inside the airport and is not used for

long-distance transportation, it does not need a large amount of energy for daily operation, and

thus it does not expressly require a large battery. According to the operating scenarios defined

in Chapter 5.2 and based on the simulation results, the electric truck consumes a maximum of

41.4 kWh per day. Taking the decrease in capacity due to battery aging into account, 20% of

the total battery capacity is added to the calculated amounts of energy. This results in an

average required battery capacity of approximately 52 kWh which means that only half of the

battery currently used for the electric truck, which has a capacity of 113 kWh, is needed. This

saves half the price of the battery in the acquisition costs and also applies to the replacement

battery in scenario 2. At the same time, this improves the environmental balance of the electric

truck, as will be explained in the following chapter, and avoids increasing vehicle weight. In

addition, the public charging infrastructure can be used instead of a private charging station,

thereby saving on the purchase price. The present values of cost differences based on these

assumptions are listed in Table 5-5.

Under these assumptions, the difference in acquisition costs between the two trucks is reduced

to €108,922, making the acquisition of an electric catering lift truck about 1.9 times more

expensive than a conventional diesel truck. In addition, in scenario 2, the cost savings in the

operating phase increase by halving the size of the replacement battery, but on the other hand,

in both scenarios, the residual value of the battery in the recovery phase is also halved.

Table 5-5: Comparison of the TCO of the two trucks based on halving the battery capacity

and eliminating the charging station

Scenario 1

Scenario 2

Acquisition Phase

€108,922

Operation Phase

– €80,128

– €81,462

Recovery Phase

– €4,705

– €6,273

Total

€24,089

€21,187

As a result, the total additional costs of the electric truck over the entire life cycle are reduced

to €24,089 in scenario 1 and €21,187 in scenario 2. This corresponds to a reduction of at least

76–81% of the total cost difference in the initial situation and makes the TCO of the electric

truck only around 6–7% higher than that of the conventional diesel truck.

However, it should be noted that the results of the TCO analysis only relate to the catering lift

truck models that are considered in this work and used under specified operating conditions

and the costs incurred by the various vehicle models can vary widely. The calculation of the

TCO only includes those costs that represent the most important cost components of a catering

lift truck, and the unimportant costs are neglected. The results of the analysis thus represent

the first indications for an economical approach of a predictive nature for the trucks examined

here.

5.6 Sensitivity Analysis

The TCO analysis is always fraught with uncertainties, as forecasts are made for the

parameters considered in the future. In order to map possible uncertainties, the influence of

parameters whose future development is subject to a high degree of uncertainty, and at the

same time are of high relevance for the TCO calculation, is examined in the form of sensitivity

analyses. For this purpose, a relative change of the respective cost parameter is assumed,

based on the initial situation. The cost parameters are varied independently of each other, i.e.,

while one parameter is being changed, the other parameters are kept constant. A combination

of the sensitivities is not taken into account here.

In the following, the future parameters of the fuel price, electricity price, and battery price are

considered particularly relevant for the economic comparison. In addition, however, the effect

of the variation in maintenance costs is examined within the framework of the sensitivity

analysis as they make up a large proportion of the total costs. A comprehensive sensitivity

analysis is then carried out to compare the impacts of all relevant cost parameters.

Diesel price variation

Since fuel costs make up a significant part of the total costs of the diesel truck, fuel prices are

expected to have a relevant influence on the results of the TCO analysis. The sensitivity

analysis shows how the increase and decrease in diesel prices by up to 50%, based on the

forecast for the diesel price development presented in Chapter 5.3.2, affects the results of the

TCO calculation. As Figure 5-8 shows, an increase in diesel prices leads to a significant

reduction in the TCO gap between the two trucks, whereby the more the diesel prices increase,

the more advantageous the electric truck becomes. This influence is greater in scenario 2

because the consumption is assumed to be higher.

Figure 5-8: Influence of diesel price variation on the TCO results

Electricity price variation

Analogous to the sensitivity analysis of the diesel price, the electricity prices are changed

relative to the initial situation, which is based on the forecast presented in Chapter

5.3.3, whereby it is investigated how the TCO present value of the electric vehicle changes

when the price of electricity is decreased or increased by up to 50%. As shown in Figure 5-9,

the TCO results also fluctuate with changes in the electricity price. Similar to the change in

diesel prices, the variation in electricity prices has a greater influence in scenario 2 due to the

higher consumption compared to scenario 1. However, the additional benefit that the electric

truck can achieve when decreasing the price of electricity is less than that observed when

increasing the price of diesel, given that consumption costs represent only a small portion of

the total costs over the service life of the electric truck.

250,000

300,000

350,000

400,000

450,000

500,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TCO [€]

Relative change in diesel price

Scenario 1

Diesel Truck Electric Truck

250,000

300,000

350,000

400,000

450,000

500,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TOC [€]

Relative change in diesel price

Scenario 2

Diesel Truck Electric Truck

Figure 5-9: Influence of electricity price variation on the TCO results

Battery price variation

The comparison results in the previous section showed that the acquisition of the electric truck

is considerably more expensive compared to the diesel truck, whereby the bulk of the price

difference relates to the large battery pack. Therefore, assumptions in this regard have a major

impact on the total cost calculation.

In the context of the TCO analysis, when determining the acquisition costs, it was assumed

that the prices for batteries used in the electric catering lift truck have not changed in 2021

compared to 2018. This is based on the fact that the price development of batteries for heavy-

duty vehicles lags when compared to that of car batteries. However, advances in the

development of batteries for mobile applications can also result in greater economies of scale

in battery systems for heavy-duty vehicles in the coming years, and especially vehicles with

large batteries benefit from a favorable battery price trend. On the other hand, new

technologies in battery systems could also lead to higher costs in the heavy-duty vehicle

sector, and hence the uncertainty about the future development of battery prices for the electric

catering lift truck is examined in a sensitivity analysis. A decrease in the battery price of up to

70% as well as an increase of up to 30% is taken into account and the acquisition costs, battery

replacement costs, and residual values of the electric truck are subsequently adjusted

according to the variation in battery prices relative to the initial situation. As shown in Figure 5-

10, the variation in battery prices affects the TCO results more strongly than the previous

parameters, as a 70% reduction in battery prices would make the comparison results very

close to an overall cost parity for the electric variant with the reference diesel truck.

250,000

300,000

350,000

400,000

450,000

500,000

550,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TCO [€]

Relative change in electricity price

Scenario 1

Diesel Truck Electric Truck

250,000

300,000

350,000

400,000

450,000

500,000

550,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TCO [€]

Relative change in electricity price

Scenario 2

Diesel Truck Electric Truck

Figure 5-10: Influence of battery price variation on the TCO results

Maintenance costs variation

In contrast, the maintenance costs hardly change over time as do the prices for diesel,

electricity, and batteries. However, maintenance costs make up a large portion of the total truck

cost and thus the question arises as to what extent the high or low maintenance costs for the

catering lift truck play a role at all. Hence, maintenance costs represent an important focus in

this application and are therefore considered separately. As shown in Figures 5-11 and 5-12,

the relative variation in the maintenance costs of the respective trucks has a significant impact

on the TCO results whereby the TCO gap between the two trucks is reduced by increasing the

maintenance costs of the diesel truck or decreasing the maintenance costs of the electric

truck. However, in the case of the electric truck, the variation in maintenance costs of the

infrastructure is not considered here.

Figure 5-11: Influence of diesel truck maintenance costs variation on the TCO results

250,000

300,000

350,000

400,000

450,000

500,000

550,000

-70%

-60%

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

TCO [€]

Relative change in battery price

Scenario 1

Diesel Truck Electric Truck

250,000

300,000

350,000

400,000

450,000

500,000

550,000

-70%

-60%

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

TCO [€]

Relative change in battery price

Scenario 2

Diesel Truck Electric Truck

200,000

250,000

300,000

350,000

400,000

450,000

500,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TCO [€]

relative change in maintenance costs

of the diesel truck

Scenario 1

Diesel Truck Electric Truck

250,000

300,000

350,000

400,000

450,000

500,000

550,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TCO [€]

Relative change in maitenance costs

of the diesel truck

Scenario 2

Diesel Truck Electric Truck

Figure 5-12: Influence of electric truck maintenance costs variation on the TCO results

Comparison of the impact of all cost parameters on the TCO results

A further analysis of all cost parameters involved in Figure 5-13 shows the relative change of

the respective parameter and the corresponding percentage change in the total cost difference

resulting from the TCO of the two trucks in relation to the initial situation. The results show that

the cost parameters battery price, diesel price, and vehicle maintenance have a large

influence, while all other cost parameters only have a small influence on the difference in TCO

of the two trucks.

Battery prices in particular have a decisive impact on the profitability of the electric truck, given

that battery costs make up a significant portion of the truck costs and thus a lower battery price

would both have a positive impact on the purchase price and replacement cost of the battery.

Therefore, the impact of a battery price change is greater in scenario 2, where the battery is

replaced once during the service life of the truck, than in scenario 1, where no battery change

is assumed. A 50% decrease in the battery price reduces the total cost difference between the

two trucks by 51% in scenario 1 and by 58% in scenario 2.

The maintenance costs of the diesel truck have the second largest impact on the cost

difference and hence with an increase of 50%, the total cost difference between the two trucks

decreases by 52% in scenario 1 and by 48% in scenario 2. The maintenance costs of the

diesel truck have a greater impact in scenario 1 since they make up a higher proportion of the

total costs compared to scenario 2. Although the effect of reducing the maintenance costs of

the electric truck is similar to the effect of increasing the maintenance costs of the diesel truck,

it is lower because the maintenance costs of the electric truck are lower than those of the diesel

truck.

250,000

300,000

350,000

400,000

450,000

500,000

550,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TCO [€]

Relative change in maintenance costs

of the electic truck

Scenario 1

Diesel Truck Electric Truck

250,000

300,000

350,000

400,000

450,000

500,000

550,000

-50%

-40%

-30%

-20%

-10%

10%

20%

30%

40%

50%

TCO [€]

Relative change in maintenance costs

of the electic truck

Scenario 2

Diesel Truck Electric Truck

Figure 5-13: Influence of parameter variations on the TCO results

The diesel price has the third largest impact on the total cost difference and a 50% increase in

the diesel prices leads to a decrease in the total cost difference by 33–43%. The higher the

consumption, the greater the impact of the diesel price. On the other hand, the optimistic

-60%

-40%

-20%

20%

40%

60%

80%

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

Relative change in total cost difference

Relative change in cost parameters

Scenario 1

Interest rate Diesel price

Electricity price Battery price

Maintenance costs of diesel truck Maintenance costs of electric truck

Tax of diesel truck Tax of electric truck

-80%

-60%

-40%

-20%

20%

40%

60%

80%

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

Relative change in TCO difference

Relative change in cost parameters

Scenario 2

Interest rate Diesel price

Electricity price Battery price

Maintenance costs of diesel truck Maintenance costs of electric truck

Tax of diesel truck Tax of electric truck

development in the electricity price did not lead to a significant economic advantage for the

electric truck as in this case, a reduction of 50% means a reduction in the total cost difference

of 17–22%. Accordingly, the electricity price only plays a minor role in terms of economic

balance in this application. However, it should be noted that the development of electricity and

fuel prices cannot be completely independent of an energy management perspective, and thus

an increase in diesel prices can correlate with an increase in electricity prices.

Since no significant changes in other cost parameters are expected, the battery price remains

the cost factor with the highest potential to influence the total cost difference. Therefore, the

development of battery prices in the coming years is an important key variable to achieve cost

parity. In addition, as discussed in the previous section, a decrease in the price of the battery

and thus the acquisition costs of the truck can also be achieved by choosing the appropriate

battery size for the application, which in turn leads to reducing the cost difference between the

two trucks.

However, it should be noted that all of the scenarios discussed in the sensitivity analyses do

not represent any inferences about the probability of the respective price or cost development,

but merely depict possible ranges and thus uncertainties.

6 Ecological Analysis and Environmental Impact

To compare the environmental impact of the catering lift trucks considered, an LCA is

performed, as explained in Chapter 1.2.2.3, for each of the respective trucks. However, the

assessment only takes into account aspects that make a difference between the two truck

technologies. In the manufacturing and end-of-life phases, it is assumed that the emissions

resulting from the manufacturing and disposal or recycling of the vehicles (excluding the

battery) are equal for both trucks, and therefore are not relevant for the comparison. According

to various studies, the manufacturing and disposal of vehicles only play a very subordinate role

in the overall balance [Amarakoon et al., 2013] [Helms et al., 2016] and therefore, only the

emissions associated with battery manufacturing and battery recycling are considered here as

the focus of the analysis lies on the emissions of the operation phase. An overall ecological

assessment is carried out here on the basis of the summation of the local and global emissions

resulting from the truck operation. Thus, the analysis does not represent a complete ecological

balance of the trucks but rather shows the differences between them in this regard.

The LCA is based on the determination of the GHG effect and PED, whereby the GHG

emissions are generally used as environmental indicators. The contribution of the substance

is given relative to the global warming potential (GWP) of CO2. Furthermore, the CO2

equivalent (CO2-eq) is usually used as a metric measure for various GHGs on the basis of their

global-warming potential, by converting amounts of other gases to the equivalent amount of

CO2 [Guinee, 2002]. The PED represents the total energy consumption including all losses

from the upstream chain. Due to the lack of sufficient data available in the literature, other

environmental impacts that can arise during the life cycle of a vehicle are not considered in the

assessment, although they are also important considerations for environmental pollution.

The LCA takes the environmental impacts associated with the fuel or electricity used in the

operation phase of the entire upstream chain (WTW) into account. This includes the production

and transmission of energy from the source to the vehicle (WTT), as well as the conversion of

energy into kinetic energy in the vehicle (TTW). The calculation of emissions associated with

the operation of vehicles is based on vehicle consumption as well as the CO2 emissions per

liter of fuel or per kWh of electricity whereas the primary energy consumption represents the

final energy consumption plus all losses from the upstream chain.

In the following, the parameters involved in the LCA are first discussed after which the climate

impact of the truck operation is determined, taking direct and indirect upstream chains into

account. For this purpose, the same diesel and electricity consumption values are used that

were already determined in the TCO analysis based on the operating scenarios defined in

Chapter 5.2. As a result, the LCA emission values are calculated and compared for the two

100

trucks. Finally, the influence of important factors, such as the diesel and electricity mix, is

shown in the sensitivity analysis.

6.1 Emission Parameters

In the following, the values considered for the provision and transport of diesel and electricity

needed to operate the trucks, as well as the manufacturing and recycling of the battery used

in the electric truck, are discussed.

6.1.1 Diesel Production

Diesel fuels have recently been developed with an increasing proportion of renewable

components, to improve the balance of GHGs in conventional vehicles. One option for

achieving this is to add biofuels or renewable fuels that come from non-fossil sources and the

proportion of biofuels in diesel consumed therefore plays an important role when determining

the GHG index.

The proportion of biofuels in Germany has remained almost constant at a maximum of 7% in

recent years [Müller-Lohse, 2021] [Mrani, 2021]. Based on the legal requirements from the

RED II renewable energy directive of the EC, at least 14% of the energy consumed in road

and rail transport has to come from renewable energy sources by 2030, which should lead to

a GHG saving of 65% compared to conventional fuels.

In this work, it is assumed that the proportion of biofuels in the diesel consumed by the

conventional catering lift truck is 7% until 2030 and 14% after 2030. The values considered for

the GHG emissions and the primary energy consumption associated with the provision of

diesel fuel are based on data from the guidelines of the BMDV [Schmied & Mottschall, 2014]

based on which the WTT emissions are 3.24 kg CO2-eq/L for conventional diesel fuel and 1.92

kg CO2-eq/L for biodiesel fuel. This includes the emissions that arise from the extraction of

crude oil and the raw materials used, the production of diesel fuel, and its distribution to the

filling stations.

The TTW emissions are those that result from the final utilization of the diesel fuel in the vehicle

when it is combusted in the engine. The complete combustion of one liter of diesel corresponds

to a production of 2.67 kg of CO2 while the combustion of biodiesel produces no emissions.

The estimated values for GHG emissions and primary energy consumption for the provision of

a liter of diesel fuel in the medium term are listed in Table 6-1.

101

Table 6-1: GHG and PED factors per liter of diesel (WTW)

Parameter

2021-2030

2031-2040

GHG [kg CO2-eq/L]

3.15

3.06

PED [MJ/L]

44.50

46.30

6.1.2 Electricity Production

Since the electrically powered vehicle does not cause any emissions when converting the

battery energy into kinetic drive energy, the emissions associated with its operation are only

due to the production of the energy that the vehicle consumes which depends heavily on the

power plant mix. Electricity generated with a high percentage of coal, for example, causes

much higher emissions than electricity largely generated from renewable sources. On the one

hand, there is a problem with determining the source of the consumed electricity, because the

electricity network is like a lake since several producers feed in electricity from one side and

electricity is drawn from the other side. As a result, it is not possible to identify the power plants

that produce electricity for the end consumer. On the other hand, it is difficult to determine the

losses in the electrical energy that is produced, especially for wind energy, for example, since

not everything that is produced is consumed. Therefore, when determining the emissions from

a power plant, the average annual emissions for the electricity mix are usually used. The

present value is often used for this purpose and remains constant for the future [Wietschel et

al., 2019b].

Compared to the EU, Germany currently has a relatively high CO2 electricity mix. However,

due to the increase in energy efficiency in Germany, GHG emissions from electricity generation

have decreased noticeably in recent years. According to numerous studies and forecasts, it is

also assumed that they will continue to decrease in the future. Such a reduction is essential to

realize the political scenarios in the context of the transition of the energy supply to renewable

energies to achieve the climate targets [Greiner & Hermann, 2016].

In this work, the average annual emissions for the electricity mix are used to calculate the GHG

emissions for the electricity consumed by the electric truck. The current values are based on

information from the Federal Environment Agency (Umweltbundesamt UBA), which publishes

an annual update on the development of the CO2 emission factor in the German electricity mix

[Icha et al., 2021]. Future values for emissions are based on a study presented by the

International Institute for Sustainability Analyses and Strategies (Internationales Institut für

Nachhaltigkeitsanalysen und-strategien IINAS), in which a projection for electricity generation

scenarios for 2030 to 2050 is calculated based on the target scenario of the German

102

government's NECP [Fritsche & Greß, 2020]. Here, it is assumed that GHG emissions from

electricity generation in Germany will fall linearly from the present value in 2021. The values

for the primary energy consumption are also based on [Fritsche & Greß, 2020].

Figure 6-1 shows the estimated development of the values for global emissions and primary

energy consumption from electricity generation in Germany for the period under review. These

values are assumed to represent emissions from the entire upstream chain, which include

emissions from the provision of primary energy sources and emissions from the power plants

themselves. In addition, losses for transmission and distribution lines in the power grid of 5%

as well as losses in the charging station of 10% are assumed [Doppelbauer, 2020] while the

operation of the electric truck is emission-free.

Figure 6-1: Development of GHG and PED factors per kWh electricity mix production

6.1.3 Battery Manufacturing and Recycling

In addition to the generation of energy needed to operate the vehicle, battery production can

have a significant impact on the environmental balance of electric vehicles. Therefore, in this

analysis, the effects of the manufacturing and recycling of the battery are also considered.

The production of lithium-ion battery packs for electric vehicles is one of the most controversial

aspects of global warming. In general, information in the literature on the CO2 emissions and

primary energy consumption associated with the manufacture of a kWh of lithium-ion battery

fluctuates greatly since most of the energy used in battery production is electricity. Therefore,

the environmental impact of battery manufacturing is highly dependent on the source of the

electricity mix as well as the demand for electricity. A meta-study by the Swedish environmental

research institute IVL presented in 2017, which is probably one of the best-known studies in

6.8

6.9

7.0

7.1

7.2

7.3

7.4

7.5

100

150

200

250

300

350

400

PED [MJ/kWh]

GHG [g CO2-eq/kWh]

GHG PED

103

this regard, estimated values for the emissions from the production of lithium-ion batteries in

the range of 150 to 200 kg CO2-eq/kWh [Romare & Dahllöf, 2017]. Two years later, in 2019,

an update of this study, with significantly lower results, was presented by the same institute,

indicating a range of 61 to 146 kg CO2-eq/kWh [Emilsson & Dahllöf, 2019]. One of the main

reasons for the lower results is the increase in the proportion of electricity from renewable

sources used in battery production to nearly 100%. However, according to the authors, this is

not the common scenario yet, although it likely will be in the future. In addition, larger

production plants and thus higher production efficiencies were also taken into account.

In a current meta-study in [Aichberger & Jungmeier, 2020] that analyzes information from

various literature sources on the life cycle assessment of lithium-ion batteries, an average

value of 120 kg CO2-eq/kWh is determined for the production of lithium-ion battery packs and

similar values can also be found in other studies such as [Peters et al., 2017]. This value is

therefore taken into account and is assumed to include the total emissions for battery

manufacturing, from the material production, through the manufacturing of the battery cells,

and up to the assembly of battery cells into packs. According to the same reference, a value

of 1,008 MJ/kWh is assumed for the primary energy consumption. On the other hand, there

are only very limited studies dealing with the end-of-life treatment of electric vehicle batteries.

Generally, these studies indicate that battery recycling can make a positive contribution to the

environmental balance. As the production of raw materials is responsible for almost half of the

GHG emissions from battery production, the substitution of raw materials with recycled

materials can reduce the environmental impact. In addition, the secondary use of vehicle

batteries, for example in stationary storage applications, could significantly improve the

balance of GHG emissions [Hall & Lutsey 2018].

Table 6-2 shows the estimated values considered for the manufacture and recycling of the

battery used for the electric catering lift truck.

Table 6-2: GHG and PED factors for battery manufacturing and battery recycling using the

current German electricity mix

Parameter

Battery

manufacturing

Battery

recycling

GHG [kg CO2-eq/kWh]

120

–20

PED [MJ/kWh]

1008

–

According to [Aichberger & Jungmeier, 2020], recycling can reduce battery life cycle emissions

by an average of approximately 20 kg CO2-eq/kWh. This value is considered in the calculations

in this work. Regarding the primary energy consumption, not enough information is available

in the literature and thus it is assumed that the energy saved is equal to the energy required in

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the recycling process. Therefore, there is no saving in the consumption of primary energy

through battery recycling.

6.1.4 Electricity Production from 100% Renewable Energy Sources

The share of renewable energies in electricity consumption is steadily growing and already

comprised 39.7% of total electricity generated in 2019, rose to 44% in 2022, and is expected

to increase further in the coming years [Destatis, 2023] [Rüger & Buchheim, 2021]. As a result,

the GHG emissions of today's electric vehicles will be lower than comparable vehicles with

internal combustion engines over the entire life cycle. Therefore, the scenario of electricity

production from 100% renewable sources is also discussed in this work. This scenario

assumes that the electricity consumed by the electric catering lift truck as well as the electricity

required for battery manufacture and recycling is completely derived from renewable sources.

Although such a scenario is not the norm today, it does appear feasible since the energy

transition away from fossil fuels to renewable sources is underway and is being promoted in

many areas. Consumers can choose whether their electricity tariff is based on the general

electricity mix or whether explicitly only green electricity from renewable sources is used.

The estimated values for GHG emissions and primary energy consumption considered in the

100% renewable electricity scenario are listed in Table 6-3.

Table 6-3: GHG and PED factors when using electricity 100% renewable energy

Parameter

Electricity

production

Battery

manufacturing

Battery

recycling

GHG [kg CO2-eq/kWh]

0.008

–10

PED [MJ/kWh]

3.67

505

–

The values for electricity production are based on the information from [Fritsche & Greß, 2020],

which provides data on cumulative energy consumption and associated GHG emissions for

Germany's electricity generation mix up to 2019 as well as projections up to 2050. The values

for electricity from onshore wind power plants are considered here, since wind power has the

largest share in renewable energy sources and are assumed to remain constant for the period

under review.

The values for battery manufacturing and recycling are based on [Emilsson & Dahllöf, 2019],

[Aichberger & Jungmeier, 2020], and further assumptions. The estimated values for the

production and recycling of lithium-ion batteries using the renewable electricity mix are taken

into account.

105

6.2 Results of the Life Cycle Analysis (LCA)

The ecological balance of both truck technologies considered is evaluated in terms of GHG

emissions and PED based on the approach discussed in the preceding sections and the results

are shown in Figures 6-2 and 6-3 respectively.

Figure 6-2: Comparison of the overall emission impact according to the defined operating

scenarios

Figure 6-3: Comparison of the overall PED according to the defined operating scenarios

-50

100

150

200

250

300

Diesel Truck Electric Truck

(electricity

mix)

Electric Truck

(renewable

electricity)

Diesel Truck Electric Truck

(electricity

mix)

Electric Truck

(renewable

electricity)

Scenario 1 Scenario 2

WTW GHG [t CO2-eq]

WTT TTW Battery manufacturing Battery recycling

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Diesel Truck Electric Truck

(electricity

mix)

Electric Truck

(renewable

electricity)

Diesel Truck Electric Truck

(electricity

mix)

Electric Truck

(renewable

electricity)

Scenario 1 Scenario 2

WTW PED [GJ-eq]

WTT TTW Battery manufacturing Battery recycling

106

As shown in Figure 6-2, even when using today's German electricity mix, the electric truck has

a better ecological footprint in both operating scenarios compared to the diesel truck whereby

the advantage of the electric truck is further reinforced when electricity from exclusively

renewable energy sources is used. In the case of the electricity mix, the WTT emissions for

providing electricity are higher than the WTT emissions for providing diesel. However, on the

other hand, the electric truck does not cause any emissions when converting the electric

energy of the battery into kinetic drive energy, so its TTW emissions are zero while the

combustion of diesel fuel in the engine causes high emissions, so that its TTW emissions

represent the largest contribution to the overall balance of the diesel vehicle. Additional

emissions for the electric truck are caused by the battery although savings can be made by

recycling the battery.

Concerning the primary energy demand, it is evident from the results in Figure 6-3 that the

electric truck has a lower overall PED than the diesel truck. The TTW energy represents how

much final energy is used by the vehicle during operation whereas the WTT energy represents

how much energy is needed to produce that final energy. Thanks to the high efficiency of the

electric motor, the TTW energy of the electric truck is much lower than that of the diesel truck.

However, in the case of using an electricity mix, the electric truck has a higher WTT energy

than the diesel truck, which indicates that providing electricity requires more energy than

providing diesel. However, in the case of using renewable electricity, the required WTT energy

of the electric truck is significantly reduced, because the efficiency of renewable energy plants

is very high, namely close to 100%. The PED required for battery manufacturing accounts for

only a small proportion of the total energy requirement of the electric truck.

However, the environmental impacts of the trucks are highly relevant to the consumption and

the higher consumption in scenario 2 led to more advantages for the electric vehicle in the

operation phase compared to scenario 1. On the other hand, the additional emissions and

energy demand caused by the battery replacement in scenario 2 reduces the total advantage

of the electric vehicle compared to scenario 1 although the additional environmental impact

caused by the battery is offset by the savings in the operation phase in both scenarios.

The ecological advantage of the electric truck heavily depends on the various electricity plants.

As the results show, the option of using 100% renewable electricity helps to further improve

the environmental balance of the electric truck, and the assumed reduction in GHGs and the

PED are primarily due to the difference in electricity production. However, another difference

in the environmental considerations relates to battery manufacturing.

The overall ecological assessment shows that the electric catering lift truck is much more

climate-friendly than the diesel truck. With the current German electricity mix, it produces

around 62.9% less GHG emissions in scenario 1 and 61% less in scenario 2 than the diesel

107

truck. When using 100% renewable electricity, the savings in GHGs are much higher as the

electric truck would have 95.9% less GHG emissions in scenario 1 and 92.4% less in scenario

2. The emission reduction in the electricity used refers not only to the electricity used to operate

the truck, but also to the production of the battery, and in Scenario 2 this also includes the

replacement battery. These very large reductions, which amount to approximately two thirds

today, confirm that the use of electric catering lift trucks is an effective way to improve domestic

emissions at airports, with a possibility to further reduce emissions with the use of renewable

electricity.

The aggregated saving in PED over the entire truck service life is 41.7% in scenario 1 and

40.8% in scenario 2. By using 100% renewable electricity, the saving is increased to around

65.9% in scenario 1 and 65.5% in scenario 2.

6.3 Sensitivity Analysis

As the results in Chapter 6.2 have shown, it is primarily the electricity mix that determines to

what extent an electric truck is advantageous in terms of the CO2 balance compared to an

internal combustion engine. The results of the option of using 100% renewable electricity

showed a significant improvement in the ecological balance of the electric truck. However,

given the developments achieved in emissions from conventional internal combustion engine

drives, this advantage must be put into perspective and, therefore, the potential change in

diesel emissions should be considered, as this would also have an impact on the results. Also

in terms of battery manufacturing emissions, they represent a fair part of the total life cycle

emissions of the electric truck and their variation can thus play an important role. In addition,

consumption also plays an important role in demonstrating the benefit of the electric truck,

because the higher the consumption, the greater the savings that the electric truck will achieve

in terms of GHG emissions.

Therefore, in the following, the effect of the variation in emissions of electricity production,

diesel production, and battery manufacturing, as well as the variation in the consumption on

the results of LCA for GHG emissions will be examined in a sensitivity analysis. For this

purpose, the values of the respective parameters will be varied by up to 50% based on the

initial values that were considered in the calculation of the LCA. While one parameter is

changed, the others are held constant. Figure 6-4 shows the influence of the variation of the

respective parameters on the resulting percentage savings in GHG emissions. The results

show a very strong dependence on electricity production, while the other parameters have less

influence in both scenarios.

108

Figure 6-4: Influence of parameter variations on the LCA results

The total savings in GHG emissions can be increased to about 76–78% by reducing emissions

from the current electricity mix production by 50%. In contrast, the total saving in GHG

emissions drops to about 46–47% if a poorer electricity mix with 50% more emissions is used.

The variation effect is not significant when other parameters are concerned as the advantage

of the electric truck increases, but only slightly with decreased emissions from battery

manufacturing, or with increased emissions from diesel production. When the consumption of

the trucks increases, the advantage of the electric truck also increases.

40%

50%

60%

70%

80%

90%

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

Saving in GHG emissions

Relative change in emission parameters

Scenario 1

Electricity production Diesel production

Consumption Battery production

40%

50%

60%

70%

80%

90%

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

Saving in GHG emissions

Relative change in emission parameters

Scenario 2

Electricity production Diesel production

Consumption Battery production

109

7 Noise Emission Measurement

In addition to global GHG emissions, noise emissions are also of relevance for environmental

pollution and the reduction in noise emissions in the context of occupational safety and public

acceptance of flight operations is of increasing importance. In the speed range relevant for a

catering lift truck, up to 40km/h, the noise of the drive dominates over the rolling noise.

Therefore, a reduction in noise emissions is expected due to the relatively low noise emissions

of the electric drive compared to diesel engines. Moreover, electric motors are switched off

during standstill phases, which is especially useful because the truck often has to stop while

driving on the airport apron, for example, because it has to wait while an airplane is being

towed. This results in great potential for reducing noise pollution.

As part of this work, a concept for acoustic measurement was developed to enable an

assessment of the noise emissions of the catering lift trucks. For this purpose, acoustic

standards and measurement methods were initially discussed to derive a suitable concept for

the acoustic evaluation of the trucks under consideration. In addition, the measuring

environment and the measuring equipment utilized are presented, whereby the weather data

and conditions are also taken into account. Finally, the results of the measurement concept for

both trucks under consideration are presented to get an overview of which truck is acoustically

friendlier.

7.1 Measurement and Assessment Parameters

A sound signal measurement is based on changes in the air pressure caused by a sound

source. Therefore, in sound measurements, the sound pressure level is usually recorded and

described with a characteristic value as similar as possible to human perception whereby the

sensitivity of human hearing strongly depends on the frequency of the sound signal. The

human ear can generally perceive sound with a frequency range between 16 Hz and 20 kHz.

However, the sensitivity for low and high frequencies is lower than for medium frequencies. In

particular, the ear is most sensitive in the frequency range from 1 to 5 kHz [Henn et al., 2001].

In the evaluation of sound events, a frequency-dependent adjustment of sound pressure levels

is usually carried out in which the sound pressure level of the measured sound signal is

weighted using specific filters to be adapted to the human hearing perception. A, C, and Z

weighting filters are generally used here according to the IEC 61672-1 standard whereas the

B and D weighting filters are no longer relevant in recent applications. The A-weighting filter

approximates the frequency sensitivity of the human ear to normal ambient noise and hence it

is used in most practical applications. The signal is amplified in the range between 1 and 5

kHz, while in the low-frequency range, the signal effects are not included due to the strong

110

attenuation of sound pressure level values [Maute, 2006]. The much flatter C-weighting filter

is typical for measurements of high sound pressure levels and nowadays it is used in the sound

measurement of flight noises. The Z-weighting (zero) filter produces a flat frequency response

between 10 Hz and 20 kHz which basically has the same effect as if there would be no filter at

all, which means that there is almost no change in the original measured signal.

In addition to frequency weighting, time weighting is used to average fluctuating measured

sound pressure levels over time. Two different time weightings are described in the IEC 61672-

1 standard: fast time weighting (F) with an evaluation time of 125 ms, and slow time weighting

(S) with an evaluation time of 1 s. The F-weighting reacts more rapidly to a sudden change in

sound level and thus it is used in most situations when evaluating sound levels with rapid

successive fluctuations. The S-weighting on the other hand reacts slowly to level changes and

is thus more suitable for the evaluation of stationary, low-impulse sound events [Feldmann,

2018].

Depending on the purpose of the measurement, there are different options for specifying the

sound pressure level. The equivalent continuous sound pressure level 𝐿𝑒𝑞 is a widely used

parameter in measurements with sound levels that fluctuate over time. It represents the

average of the sound pressure levels within an observation period. In addition, the maximum

sound pressure level 𝐿𝑚𝑎𝑥 and the sound exposure level 𝐿𝐸 are also used whereby 𝐿𝑚𝑎𝑥

denotes the highest sound pressure level that occurred during the measurement period. 𝐿𝐸 is

usually used to measure people's exposure to sound, where the total sound energy of a

measurement of any length is referenced to a 1 second period and specified as a level and

thus enables a direct comparison between sound measurements for different periods of time.

According to [Feldmann, 2018], it is used to assess individual sound events such as overflights

or trucks driving by.

For the evaluations in the context of this work, the A and Z frequency weighting filters as well

as F time weighting filter are relevant. The measurement parameters that are used for the

evaluation of the noise emissions of the catering lift trucks considered are shown in Table 7-1.

The Z-weighting is intentionally used in this work to represent sound level values over the

measurement period, because this corresponds approximately to the linearity and thus hardly

any deviations result, while A-weighted sound pressure level values are used to enable a direct

comparison between the two trucks. In addition, the sound pressure levels are represented

with the help of the third-octave spectrum which is an assessment based on frequency bands

in which the frequency range from 16 Hz and 20 kHz, which is audible to humans, is divided

into 32 third-octave bands that represent filters with relatively constant bandwidth.

111

Table 7-1: Parameters used for evaluating measured noise emissions

Parameter

Representation

𝑳𝒁𝑭

The Z-weighted and F-weighted sound pressure level

𝑳𝒁𝒆𝒒

The equivalent continuous (average) of the Z-weighted sound pressure

levels over the measurement period

𝑳𝑨𝒆𝒒

The equivalent continuous (average) of the A-weighted sound pressure

levels over the measurement period

𝑳𝑨𝑭𝒎𝒂𝒙

The maximum value of the A-weighted and F-weighted sound pressure

levels over the measurement period

𝑳𝑨𝑬

The average of the A-weighted sound pressure levels related to 1

second

7.2 Measurement Equipment and Logging Data

The NTi Audio XL2 hand-held acoustic analyzer was used for all noise measurements in this

work. The analyzer has an omnidirectional free-field microphone of accuracy class 2, which is

particularly suitable for use in research and development purposes [NTi Audio, 2022]. The

microphone features a 1⁄4-inch condenser microphone capsule with permanent polarization

and the possible sound pressure level range is from 30 to 130 dB. Additional characteristics of

the microphone are shown in Appendix E.

According to the standard ISO 3746, windscreens for microphones must be used when

measuring outdoors [ISO, 2010b]. Therefore, an open-pored foam ball was placed on the

measurement microphone capsule, which reduces the interference from wind.

The measurement data obtained from the measurement device are represented in values of

the weighted sound pressure level with the frequency weightings A and Z and the time

weightings F. Values for the equivalent continuous, and the maximum and minimum sound

pressure levels are also provided, and the real-time information of the measurement is included

in the recorded measurement data. The signals were saved in 100 ms steps and the reading

and evaluation of the measurement data were subsequently carried out using MATLAB.

7.3 Measurement Concept

The measurement concept is divided into two different measurement setups, namely the

measurement setup for the truck drive-by and the measurement setup for the operation of the

lifting system. In the following, the concepts of the two measurement setups are first explained,

and then the results of both trucks are presented and compared separately for each

measurement setup.

112

7.3.1 Measurement Setup for the Drive-By

The concept of the drive-by measurement is based on the content of the standards ISO 362-1

and DIN 45642. The ISO 362-1 standard specifies a procedure for measuring the noise

emissions from road vehicles when accelerating under defined and repeatable conditions,

which allows different vehicles to be compared with one another. The sound pressure level is

measured exactly when accelerating to 50 km/h at full throttle [ISO, 2015]. The DIN 45642

standard defines procedures for determining the noise emissions from road traffic by

measuring individual event levels or the maximum drive-by levels. The noise emission is

described by specifying the equivalent continuous sound pressure level. Due to the movement

of the sound source, a larger safety distance is required for the drive-by measurement setup

[DIN, 2004].

Figure 7-1 shows the drive-by measurement setup. One microphone is used and is fixedly

positioned at a height of 1.2 m above the road surface and at a distance of 7.5 m from the

center of the acceleration path C-C. The reference axis of the microphone is aligned so that it

points perpendicular to the measuring surface. While the vehicle is approaching, i.e. up to line

A-A, which represents the beginning of the measuring section, the engine speed should be

75% of the driving speed of 50 km/h. After crossing line A-A, the vehicle must be accelerated

at full throttle until it has completely crossed line B-B. The accelerator pedal is then immediately

placed in the neutral position.

Figure 7-1: Concept of the drive-by noise measurement setup

According to the above standards, the interference by extraneous noises must be at least 10

dB lower than the maximum drive-by level. Alternatively, depending on the discrepancy

between the interfering sound and the sound event being measured, correction values are

specified to correct the results that are falsified by extraneous noise. In addition, the

113

measurement is only valid if the sound pressure level rises by at least 10 dB while driving by

and then drops again by 10 dB.

The measuring site is standardized in accordance with the standard ISO 10844, which

specifies requirements for test tracks used to measure noise emissions from road vehicles and

their tires. This states that acoustic measurements should be carried out at an ambient

temperature of 5 to 40 ℃ and preferably at wind speeds below 5 m/s but maximally below 10

m/s [ISO, 2014].

To obtain a sufficient range of data, three separate drive-by driving measurements were carried

out with the diesel and the electric trucks respectively and the measurements with the best

measurement data were used for the evaluation. The measuring point remained fixed in all

measurements. In accordance with the standards recommendations, the measurements were

carried out outdoors in an acoustic-free field and external noises as well as extreme ambient

conditions that have an unfavorable effect on the measurement, such as strong wind,

extremely high or low temperatures, or humidity, were avoided during the measurements.

7.3.2 Measurement Setup for the Lifting System

Since the lifting system is only operated when the truck is idling, the noise emissions of the

lifting system were measured using a measurement method for stationary noise sources based

on the standard ISO 3744 [ISO, 2010a]. However, according to this standard, the sound

pressure levels should be measured along a three-dimensional envelope that encloses the

sound source, which could not be implemented in this application and thus the procedure was

modified in relation to the work conditions.

As with the drive-by measurement setup, one measurement point with one microphone is used.

The microphone is likewise positioned 1.2 m above the road surface, with its reference axis

aligned perpendicular to the measuring surface. For stationary measurements, however, the

microphone can be placed significantly closer to the source of the sound, and it was therefore

placed at a distance of 1 m from the vehicle contour in accordance with the recommendations

of the ISO 3744 standard. The measuring point was chosen in such a way that the measuring

distances to both of the presumed sound sources, the truck drive and the hydraulic system,

were the same.

One measurement for lifting and one measurement for lowering was carried out with the diesel

truck and the electric truck respectively. The measurement duration for lifting includes raising

the container until the maximum height while the lowering measurement duration includes

lowering the container from the maximum lifting position back to its rest position. The

114

measurements were carried out at the same location and under the same measuring

conditions as the drive-by measurements.

7.4 Measurement Results

The measurements were carried out on October 26, 2017, on the site of LSG Sky Chefs at

Frankfurt Airport using the acoustic analyzer XL2, shown in Appendix E. The diesel truck was

first tested acoustically, both when driving by and when lifting/lowering the container. Then the

electric truck was tested, both when driving by and when lifting/lowering the container. The

results of the measurement campaigns are presented in the following sections. First, the

ambient noise measurement and the meteorological data collected during the measurements

are presented. After that, the results of both trucks obtained with the two measurement setups

are presented and compared.

7.4.1 Recording of Environmental Noise

Since the acoustic measurements are carried out outdoors, the ambient conditions at the time

of the measurements must be considered and thus the environment noise at the measurement

site was measured for 14 seconds. As shown in Figure 7-2, the acoustic values of the

environment are considered to be relatively high, with a maximum sound pressure level of 69.8

dB. As the measuring point was far away from the airport and there was neither aircraft noise

nor traffic noise, the wind is considered to be the main reason for these values. However, the

maximum sound pressure level was sufficiently below the measured sound events and

therefore no correction was needed for the measurement data.

Figure 7-2: Evaluation of the ambient noise

LAeq LAFmax LAE

63.30 69.80 74.60

Sound pressure level [dB (A)]

115

In addition, weather data were collected for the time of measurements to ensure that the

meteorological parameters are within the normative limits and that the acoustic measured

values are valid. For this purpose, the meteorological data were requested from the German

Weather Service, which has a measuring station at Frankfurt Airport. On the day when the

measurements were taken, the air temperature ranged between 6.4 °C and 16.7 °C and it was

a relatively sunny day with a rainfall of 0 L/mm2. However, for the wind speed, a maximum of

8.9 m/s was recorded. Although it is not below the preferable upper value, which is 5 m/s, it is

below the maximum acceptable value of 10m/s.

7.4.2 Noise Emission from Drive-By

Figure 7-3 shows the drive-by measurement results represented in the time course of the Z-

weighted and F-weighted sound pressure levels of both catering lift trucks. The comparison

shows a clear difference between the values of the electric truck and those of the diesel truck

where the acoustic event occurs, that is when the truck passes by the measurement point from

second 10 to second 18. Before this point, the values for the two trucks are similar and, at

some points, the sound pressure level of the electric truck is even higher than that of the diesel

truck. While this seems surprising at first, since low levels of noise emission are to be expected

with the electric motor over the entire measurement, the measurement data before second 10

represent the ambient noise. This is because the starting point of the truck is approximately 20

m away from the measurement microphone, and thus external sound sources can contribute

to the measurement.

Figure 7-3: Sound pressure level time course of the drive-by measurement

0 2 4 6 8 10 12 14 16 18

LZF

[dB (Z)]

Time [s]

Diesel truck Electric truck

116

Figure 7-4 provides a comparison of the A-weighted sound pressure level values of the two

trucks. As indicated, the values of the noise emission from the electric truck are less than that

from the diesel truck. The 𝐿𝐴𝑒𝑞 value, which represents an average value over the

measurement period, shows a difference in favor of the electric truck. Also, when looking at

the values of the maximum sound pressure level 𝐿𝐴𝑚𝑎𝑥, there is a noticeable difference

between the maximum values recorded for the two trucks of 4.2 dB. What most clearly confirms

this is the value of 𝐿𝐸, since, as already mentioned, it is used to compare measurements

regardless of their durations and it shows that people are more exposed to noise from diesel

trucks than from electric trucks. Hence, according to these results, the electric truck is

considered to have lower noise emissions compared to the diesel truck when driving.

Figure 7-4: Evaluation of the drive-by measurement

Furthermore, the measured drive-by noise is analyzed according to the frequency bands and

Figure 7-5 shows the third-octave spectrum of the sound pressure level (represented with 𝐿𝑍𝑒𝑞)

for the drive-by measurement. As shown in the figure, the two trucks have relatively similar

spectrum courses since the sound pressure levels are high at low frequencies and decrease

with high frequencies. It is noticeable, however, that the sound pressure level of the electric

truck is generally lower than that of the diesel truck whereby the difference is particularly

evident in the frequency bands 160 to 315 Hz and 8,000 to 20,000 Hz. However, in the

frequency range below 20 Hz, the sound pressure level of the electric truck is a little higher

than that of the diesel truck, which is probably due to the contribution of the external ambient

sound sources before the drive-by event as explained previously.

LAeq LAFmax LAE

Diesel truck 72.70 83.80 85.40

Electric truck 69.50 79.60 82.10

Sound pressure level [dB (A)]

117

Figure 7-5: Third-octave spectrum of the drive-by measurement

7.4.3 Noise Emission from Lifting System

In this measurement setup, the noise emissions from the lifting system of both trucks were

measured while lifting and lowering the catering container, and Figures 7-6 and 7-7 show the

sound pressure level time course of the lifting and lowering measurements respectively. As is

evident from the two figures, the sound pressure level of the noise from the electric truck is

lower than that of the noise from the diesel truck in both measurements.

Figure 7-6: Sound pressure level time course of the lifting measurement

LZeq

[dB (Z)]

Frequency [Hz]

Diesel truck Electric truck

010 20 30 40 50

LZF

[dB (Z)]

Time [s]

Diesel truck Electric truck

118

Figure 7-7: Sound pressure level time course of the lowering measurement

However, since the time required to raise and lower the container depends on the hydraulic

conditions, the measurement duration cannot be specified, which led to differences in the

measurement durations between the two trucks, as can be seen in the two figures. The lifting

measurement took 52 seconds for the diesel truck and 50 seconds for the electric truck, while

the lowering measurement took 59 seconds for the diesel truck and 67 seconds for the electric

truck.

It is noted that at the end of the lifting measurement, when the container reaches its final

position on top, the sound pressure level of the diesel truck falls back to approximately the

level it started with at the beginning of the measurement whereas for the electric truck, the

measurement apparently ended before the container settled in its final position and therefore

the sound pressure level at the end of the measurement did not fall to the same level at the

beginning of the measurement. The opposite applies to the lowering measurement, in which

the measurement of the electric truck is continued until the container settles in its lower position

and the sound pressure level thus falls back to its initial level whereas, for the diesel truck, the

measurement seems to end a little earlier than that. This affects the calculation of the

comparative sound pressure level values shown in Figure 7-8.

However, as it turns out, the 𝐿𝐴𝑒𝑞 and 𝐿𝐴𝐸 values are inconsistent in the measurements, as in

the lifting measurement, these values are higher for the electric truck than for the diesel truck,

while for the lowering measurement the values are in favor of the electric truck, with a relatively

large difference to those of the diesel truck. This discrepancy can therefore be explained by

the different durations and endings of the measurements.

010 20 30 40 50 60

LZF

[dB (Z)]

Time [s]

Diesel truck Electric truck

119

Figure 7-8: Evaluation of the lifting system measurement

In comparison, for the diesel truck, a maximum sound pressure level of 87.2 dB was recorded

in the lifting measurement and 82.5 dB in the lowering measurement whereas, for the electric

truck, a maximum sound pressure level of 82.8 dB was recorded in the lifting measurement

and 79.8 dB in the lowering measurement. This results in a difference of 4.4 dB in the lifting

measurement and 2.7 dB in the lowering measurement, giving a clear indication that powering

the lifting system with an electric drive is more noise-friendly than with a diesel drive. This is

also confirmed by the clear difference in the course of sound pressure level over time of the

two trucks, shown Figures 7-6 and 7-7.

The lifting system generally shows higher levels than the drive-by measurements and there

are probably two reasons for this: firstly, because the measuring point is much closer to the

sound source, and secondly, in addition to the drive, the hydraulics also contribute to these

levels, which appears to be the dominant sound source. In addition, there is a safety feature

that beeps when the container is raised and lowered, which contributes to additional noise.

A comparison of the third-octave spectrum of both trucks is shown in Figures 7-9 and 7-10 for

the lifting and lowering measurements. As in the drive-by measurement, the sound pressure

levels in the measurements of the lifting system are higher with low frequencies and decrease

with high frequencies. The illustration shows hardly any differences in the lifting measurement,

as the sound pressure levels of the two trucks converge across the frequency spectrum and

the values for both trucks simply fluctuate up and down. However, a distinct difference between

the sound pressure levels of the two trucks can be seen in the lowering measurement and it is

clear that the values of the diesel truck are higher than those of the electric truck over most of

the frequency spectrum.

LAeq LAFmax LAE LAeq LAFmax LAE

Liftitng Lowering

Diesel truck 78.20 87.20 95.40 77.80 82.50 95.50

Electric truck 79.80 82.80 96.90 71.00 79.80 89.30

100

Sound pressure level [dB (A)]

120

Figure 7-9: Third-octave spectrum of the lifting measurement

Figure 7-10: Third-octave spectrum of the lowering measurement

LZeq

[dB (Z)]

Frequency [Hz]

Diesel truck Electric truck

LZeq

[dB (Z)]

Frequency [Hz]

Diesel truck Electric truck

121

8 Possible Improvements for the Lifting System

As the energy balance analysis in Chapter 4 showed, a lot of energy is lost in the hydraulic

circuit of the lifting system, which leads to a very low energy efficiency of the system. Therefore,

in this chapter, some improvement potentials in the lifting system and their impact on the

overall efficiency of the system are discussed.

8.1 Reduce Pressure Losses

In addition to energy losses due to component efficiencies, there are also pressure losses in

the hydraulic circuit, which can be minimized in order to increase the overall efficiency of the

lifting system. The most striking of these losses are the basic pressure losses due to the load

sensing and the pump relief functions, which cause a continuous drop in system pressure of

approximately 27 bar over the entire work cycle. This is shown in Figures 8-1 and 8-2 for both

trucks and is represented by the difference between the system pressure and the pressure

required for the function being operated. However, in the electric truck, this only applies to the

container and support legs functions as the platform and platform tongue functions are no

longer hydraulically driven. This pressure drop results in losses of approximately 30.4% in the

diesel truck and 25.6% in the electric truck of the total energy consumed for a complete lifting

cycle.

Therefore, reducing these pressure losses is the most appropriate measure to reduce energy

losses and thus increase the efficiency of the lifting system. This could be achieved for example

by selecting more appropriate components or even by optimizing setting of the relevant

components according to the hydraulic circuit design. Since the pump currently used is a fixed

displacement pump, these losses cannot be reduced to zero. However, by using a variable

displacement pump, for example, not only pressure losses could be avoided, but the flow rate

will be also adjusted to the pressure required by the consumer, avoiding too much oil being

delivered by the fixed displacement pump that is not used and has to be returned to the tank

again, which represents additional hydraulic losses in the system.

Using the simulation model, the effect of reducing pressure losses by up to 50% based on the

initial value on the efficiency of the lifting system is examined. The results are listed in Tables

8-1 and 8-2 for the two trucks. As the results show, the variation in the pressure losses has a

significant effect on the efficiency, as reducing the pressure losses by 50% increases the

efficiency of the lifting system by 17.88% for the diesel truck and 14.35% for the electric truck.

However, this is reflected in a relatively small increase in the overall vehicle efficiency, which

is almost the same for both trucks at about 0.6%, since, as explained in Chapter 4.3, the

122

consumption of the lifting system generally only represents a small proportion of the total

consumption of the truck compared to the consumption of the driving system.

Figure 8-1: Pressure losses in the lifting system of the diesel truck

Figure 8-2: Pressure losses in the lifting system of the electric truck

100

150

200

250

020 40 60 80 100 120 140 160 180 200 220

Pressure [bar]

Time [s]

System pressure Function pressure

100

150

200

250

020 40 60 80 100 120 140 160 180 200 220

Pressure [bar]

Time [s]

System pressure Function pressure

123

Table 8-1: Influence of variation in pressure losses on energy efficiency of the diesel truck

Pressure losses

[bar]

𝜼𝑬 Lifting system

[%]

∆𝜼𝑬 Lifting system

[%]

∆𝜼𝑬 Entire truck

[%]

100%

11.08%

0.00%

90%

11.42%

3.08%

0.12%

80%

11.79%

6.42%

0.24%

70%

12.19%

9.98%

0.37%

60%

12.61%

13.79%

0.49%

50%

13.06%

17.88%

0.61%

Table 8-2: Influence of variation in pressure losses on energy efficiency of the electric truck

Pressure losses

[bar]

𝜼𝑬 Lifting system

[%]

∆𝜼𝑬 Lifting system

[%]

∆𝜼𝑬 Entire truck

[%]

100%

25.84%

0.00%

90%

26.50%

2.50%

0.12%

80%

27.20%

5.22%

0.24%

70%

27.95%

8.10%

0.37%

60%

28.73%

11.13%

0.49%

50%

29.56%

14.35%

0.62%

8.2 Recovery of Potential Energy

The container lifting/lowering function provides a very good opportunity to recover potential

energy while lowering the container. As mentioned earlier, the container is lowered with the

help of its own weight only, however, the released energy in the process is currently being lost

and is therefore no longer available to the system. If this energy could be recovered, the energy

consumption would decrease, and the overall efficiency of the lifting system would increase

accordingly. Therefore, the potential of this possibility in the electric truck was investigated with

the help of the simulation model, and the results were evaluated and compared with those of

the current lifting system from the energy efficiency point of view. When lowering the container,

it is assumed that the potential energy of the container, resulting from its weight and the weight

of the payload, forces the hydraulic pump to rotate in the opposite direction, acting as a

hydraulic motor and converting the hydraulic energy into mechanical energy. This energy is

124

further converted into electrical energy by the electric drive, which in this case acts as a

generator, and fed to the battery via the AC/DC converter and stored there.

Figure 8-3 shows the energy distribution for the process of lifting/lowering the container with

potential energy recovery for different payloads. The positive segments in the diagram

represent the energy used for lifting the container. This includes the potential energy of the

load, which is the usable energy, and the energy lost in the system during the lifting process.

The negative segments represent the portion of the potential energy that can be recovered,

while the rest is lost again in the system during the lowering process due to the efficiency of

the components. It should be noted, however, that in the current lifting system, although no

supply pressure is required when lowering the container, the pump operates at the minimum

system pressure to keep the hydraulic circuit running, resulting in energy losses, which are

avoided in case of recovery.

Since the own weight of the container itself is large, lifting the container even when unloaded

consumes a significant amount of energy, while offering a significant potential energy that can

be recovered when lowering. The energy savings increase with increasing payload, because

the larger the lifted load, the more energy available for recovery when lowering, as well as the

higher the efficiency of the electric drive. Depending on the payload weight, 37.81–40.23% of

the available potential energy can be recovered, which corresponds to 11.18–14.14% of the

energy used for lifting the container.

Figure 8-3: Energy distribution of the lifting/lowering process

Table 8-3 shows the impact of potential energy recovery on increasing the energy efficiency

of the electric truck for different payloads. Considering a complete work cycle, by recovering

the potential energy when lowering the container, an increase in the energy efficiency of the

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000

Energy [kWh]

Payload [kg]

Potential energy Energy losses Recovered energy

125

lifting system of 21.48–22.53% can be achieved, which is reflected in an increase in the overall

vehicle energy efficiency of 0.83–0.92%.

However, higher potential energy recovery can be expected by increasing the efficiency of the

lifting system components. In addition, potential energy recovery can be combined with other

energy-saving measures to further increase the overall vehicle energy efficiency.

Table 8-3: Influence of potential energy recovery on energy efficiency of the electric truck

Payload [kg]

𝜼𝑬 Lifting

system [%]

𝜼𝑬 Lifting system

with recovery [%]

∆𝜼𝑬 Lifting

system [%]

∆𝜼𝑬 Entire truck

[%]

21.16%

25.70%

21.48%

0.83%

500

22.07%

26.84%

21.62%

0.85%

1,000

22.92%

27.91%

21.77%

0.86%

1,500

23.72%

28.92%

21.91%

0.87%

2,000

24.48%

29.87%

22.04%

0.88%

2,500

25.19%

30.77%

22.17%

0.89%

3,000

25.84%

31.62%

22.29%

0.90%

3,500

26.48%

32.42%

22.42%

0.91%

4,000

27.08%

33.18%

22.53%

0.92%

126

9 Summary and Outlook

In the following, the results of this work are summarized. In addition, suggestions are made for

further research work to expand on some points that were not pursued further in this work,

either due to the focus on content or insufficient data that is currently available.

9.1 Summary

Battery electric trucks are considered to be one of the most promising zero-emission truck

technologies in heavy-duty truck applications due to their high efficiency and savings in energy

consumption compared to conventional trucks. However, the acquisition costs of battery

electric heavy-duty trucks are currently far higher than those of their conventional counterparts,

which represents a substantial barrier, particularly for users for whom the acquisition expenses

account for a sizable portion of the total cost of ownership. On the other hand, in addition to

the benefits of reducing emissions and noise pollution, electric trucks can achieve significant

savings in operating costs and maintenance costs. Since most of the researches dealing with

the evaluation of effectiveness of replacing traditional heavy-duty trucks with battery electric

trucks focuses on long-distance applications and some urban applications, there is a need to

research the use of battery electric trucks in local applications as well, especially those trucks

that have special-purpose ancillary systems.

In this thesis, an approach was presented to analyze and evaluate the competitiveness of a

prototype electric catering lift truck compared to a conventional diesel truck based on real

operational profile data of the catering lift trucks used at Frankfurt Airport. For this purpose,

driving cycles and operating data of the lifting system were recorded and used to determine

the realistic energy consumption of the trucks considered with the help of a simulation model.

Based on the simulation results, the energy efficiency and the energy losses of both trucks

were analyzed for the driving system and the lifting system individually as well as for the entire

vehicle.

The simulation results showed that the electric truck offers great advantages over the

conventional diesel truck in terms of increased efficiency and reduced consumption. Nearly

two-thirds of the energy consumed for driving and more than half of the energy consumed for

operating the lifting system can be saved with the electric truck. Considering a full work cycle,

including driving and operating the lifting system, the truck's overall efficiency has been

increased by about threefold through electrification.

A TCO analysis was performed to determine the economic potential and cost differences of

the two trucks over the entire service life of the truck. Since the assumed service life of the

catering lift truck is 20 years, the analysis was carried out from 2021, when the trucks and the

127

charging station for the electric truck are to be purchased, to the end of 2040. The analysis

was drawn up for two daily operating scenarios, while the possibility of replacing the battery of

the electric truck was also taken into account.

The results of the TCO analysis showed that although the electric truck has very high additional

acquisition costs, it has considerable advantages in operating costs and residual value

compared to the diesel truck. Without a subsidy, the incremental costs in the acquisition phase

cannot be offset by the cost savings in the operation and recovery phases, which makes

electrification of the catering lift truck economically unfeasible at present. However, if 80% of

the incremental costs of the electric truck and the associated charging infrastructure are

financed, its TCO will be lower than that of the diesel truck and in this case, using the electric

catering lift truck will be economically efficient for the operator.

The actual battery size required by the electric truck was also discussed based on the

simulation consumption results and the daily operating scenarios that are defined in this work.

Accordingly, if a scenario were considered in which only half the size of the battery currently

in use is used combined with forgoing the purchase of a charging station and using public

charging infrastructure instead, this would bring the TCO of the electric truck into a very close

range to that of the diesel truck.

The effect of variation in cost parameters on the total cost of ownership was shown in sensitivity

analyses in which the price of batteries, the price of diesel, and the costs of vehicle

maintenance have a significant impact, while other cost parameters only have a small impact.

Therefore, the expected decrease in the battery prices will certainly increase the economic

viability of the electric truck in the coming years.

A LCA was carried out to compare the environmental impact of the catering lift trucks studied

in this work in terms of the GHG emissions and the demand for primary energy. The analysis

considers aspects that make a difference to the environmental balance between the two truck

technologies at all phases of the truck's life cycle, including the manufacturing, operation, and

end-of-life phases. The possibility that the energy consumed by the electric truck and the

electricity required for the production and recycling of the battery come entirely from renewable

energy sources was also discussed.

The results of the LCA demonstrated that, the electric truck has a better environmental footprint

than the diesel truck even with today's German electricity mix. Although the battery causes

additional emissions, these can be offset by savings in operation emissions and emission

savings can also be achieved through battery recycling. The advantage of the electric truck is

enhanced when electricity exclusively from renewable energy sources is used, as this reduces

not only the GHGs and the PED associated with the production of electricity, but also those

associated with the manufacture of the battery. The sensitivity analysis of the emission

128

parameters showed that the advantage of the electric truck is heavily dependent on the

electricity mix. In addition, the higher the consumption, the larger the environmental benefits

of using the electric truck.

Furthermore, a concept was presented in this work that enables the measurement and

evaluation of the noise emissions of the considered catering lift trucks both when driving-by

and when operating the lifting system. The results of the noise measurements were presented

in sound pressure levels weighted with frequency weightings A and Z and time weighting F. In

addition, the third-octave spectrum of the sound pressure levels is also shown. In light of the

measurement results, it was concluded that the electric truck causes lower noise emissions

than the diesel truck when driving, as well as when operating the lifting system.

At the end of the work, the potential of some improvement measures for the lifting system and

their impact on the overall efficiency of the system was discussed. Minimizing basic pressure

losses and potential energy recovery when lowering the container in the electric truck were

considered, being the possibilities with the greatest potential for energy savings. The

respective measures were implemented in the simulation model and the results for increasing

energy efficiency were evaluated, which showed that these measures have a significant impact

on increasing the efficiency of the lifting system.

However, it should be noted that the results of this work are based on the defined operating

profile and work cycles that are presented here and, therefore, the values may vary for different

applications or different working conditions of the catering lift truck.

9.2 Outlook

The efficiency analysis of the catering lift truck presented in this work gives the possibility in

future developments to determine the potential for increasing the energy conversion efficiency

of the truck and thus reducing its consumption and associated environmental impacts.

Indeed, although the lifting system of the electric truck showed better efficiency than that of the

diesel truck, it is still very poor compared to the driving system, and therefore, it is necessary

to further investigate the possibility of increasing the efficiency of the lifting system. Even if the

lifting system only contributes a small proportion of the total consumption of the truck, given

the application as truck fleets that could be used at many airports around the world, any

improvement in efficiency can contribute to a significant reduction in energy consumption and

thus to an improvement in the environmental balance.

From a technical point of view, the application of the catering lift truck for local delivery is very

well suited to the use of electric trucks. However, the economic feasibility is the main challenge

due to the very high acquisition costs and low operating hours, and thus the savings in

129

operation costs are not enough to offset the initial cost difference. However, the acquisition

costs considered in the analysis in this work refer to the price of the prototype truck and not to

the price of a series truck, and no consideration is given to possible reductions in future truck

costs. Therefore, in future work, the effect of economies of scale on the purchase price of the

catering lift truck – and thus on the results of the TCO analysis – should be investigated.

At the same time, the high acquisition costs of the electric catering lift truck are mainly due to

the costs of the large battery, which may actually not be required with this capacity. Therefore,

given the very high cost of truck batteries at present, dimensioning the battery capacity

required in this application, by taking possible operating scenarios into account, is essential.

In addition, the selection of a suitable charging station, as well as the option of using public

charging stations for a fleet of electric trucks rather than a single truck, should also be

considered more extensively.

Finally, assumptions about some cost parameters such as maintenance costs have so far been

based on estimates, since empirical values for heavy electric trucks are still lacking in the early

stages of market availability. Since maintenance costs also make up a large proportion of the

TCO, there is a need for further research in this regard.

The approach developed in this thesis can also be applied to many other similar applications,

for example, trucks on construction sites like dump trucks or crane trucks, loaders for large

individual goods, garbage trucks and others, where the results of this work provide the basis

for a comparative view.

130

Appendix

A – Technical Specifications of the Considered Catering Lift Trucks

Table A-1 shows the technical data of the catering lift trucks considered in this work based on

the available truck datasheets and product brochures. The truck model is based on the truck

chassis of the Terberg YT182 for the diesel truck and the Terberg YT202-EV for the electric

truck.

Table A-1: Technical specifications of the catering lift truck X-Cat M from DOLL (Source:

DOLL Fahrzeugbau GmbH)

Vehicle Data

Diesel Truck

Electric Truck

Weight

Vehicle gross weight

21,000 kg

Vehicle curb weight

16,174 kg

17,234 kg

Payload

4,500 kg

3,766 kg

Drive Motor

Type

Cummins QSB6.7-190

(Euro IV)

Siemens ELFA Drive

Power

142 kW

138 kW (188 PS)

at 2000-2500 1/min

Torque

809 Nm

780 Nm

at 0-1800 1/min

Max. rotation speed

2000 1/m

2500 1/min

Min. rotation speed

800 1/m

Battery System

Type

LFMP

(Lithium-Ion

Magnesium

Phosphate)

Capacity

113 kWh (184 Ah)

Nominal voltage

614 V

Charging capacity

40-80 kW

Charging port

400 V DC

Charging time

Ca. 2.5 h

Charge cycles

2800 at 100% DoD

4800 at 80% DoD

6700 at 70% DoD

10000 at 50% DoD

131

Tires

Wheel size

11R22.5 (6 wheels)

Axle ratio

11.98:1

Transmission

Type

Allison 3000 Automatic Manual

gearbox

Gear ratio

Ratio

3.487

1.864

1.409

1.00

0.75

5.027

Speed

backward

132

B – Data Processing of the Driving Cycles

This chapter describes the procedure used for processing the recorded measurement driving

data, which is derived on the basis of the procedure presented in [Duran & Earleywine, 2012]

and in proportion to the nature of the recorded data. The data processing is carried in three

steps as follows:

1) Removing of zero-velocity drifts:

Zero-speed drifts occur when the GPS data logger is turned on while the vehicle is stopped,

as the GPS data logger records very small speed values due to the frequent interruptions and

reconnections between the GPS signal receiver and the available satellites [Duran &

Earleywine, 2012] [Gräbener, 2017]. This leads to falsifications in the distance calculations

based on the integration of the speed data and associated time data, as well as in the energy

consumption calculations, particularly for the electric truck, since it consumes no energy while

stationary. Therefore, the fluctuations in speed data recorded during these periods are

mitigated by low speed removal, whereby all speed values below a specified lower limit are set

to zero. Examining the measurement data revealed that the speed values recorded when

stationary are often less than 0.75 km/h and therefore this is considered the lower speed limit.

Additionally, any speed point that falls between zero points is also set to zero. An example of

the effect of this filtering step is shown in the Figure B-1.

Figure B-1: Filtering zero-speed drifts

2) Modifying of Signal Gaps

Measurement gaps that occur when there are temporarily not enough GPS satellites available,

such as when driving through a tunnel, have been corrected. First, the GPS signal data is

checked for the intervals where there is no satellite signal, and then values for the

corresponding speed points are interpolated based on the previous and next signal points.

-2

0 5 10 15 20 25 30 35 40 45 50 55 60

Speed [km/h]

Time [s]

Unfiltered data Filtered data

133

This also removes the abrupt zero speed values caused by temporary GPS signal interruptions

where the measured speed drops to zero for only a few milliseconds and then returns again to

the actual value. Figure B-2 shows an example of a corrected speed data gap.

Figure B-2: Filtering signal gaps

3) Denoising and Smoothing of the Signal

For denoising and smoothing the measured data, a moving-average filter is used. The effect

of this filter is shown in Figure B-3.

Figure B-3: Signal smoothing

Finally, the measurement data is further processed so that all data points recorded in one

second are represented by the mean value. This has two effects, first, reducing the intensity

of the measurement data and thus reducing the computational effort in the simulation model,

and second, removing the remaining fluctuations from previous filtering steps. Figure B-4

shows how data processing removes most of the noise and errors from the measured data

while preserving the profile of the original signal.

-5

0 5 10 15 20 25 30 35

Speed [km/h]

Time [s]

Unfiltered data Filtered data

010 20 30 40 50 60 70 80 90 100

Speed [km/h]

Time [s]

Unfiltered data Filtered data

134

Figure B-4: Signal processing of the driving cycle 1

0 100 200 300 400 500 600

Speed [km/h]

Time [s]

Original signal

0 100 200 300 400 500 600

Speed [km/h]

Time [s]

Final processed signal

135

C – Driving Cycles Recorded with the Diesel Truck

Figure C-1 shows the velocity profiles of the driving cycles recorded for the diesel truck

according to the driving routes defined in Chapter 2.3.1.1, the characteristics of which are

presented in Table C-1.

Figure C-1: Velocity profiles of the driving cycles of the diesel catering lift truck

100

0 100 200 300 400 500 600

Velocity [km/h]

Time [s]

Driving cycle 1

100

120

0 200 400 600 800 1000 1200

Velocity [km/h]

Time [s]

Driving cycle 2

0 300 600 900 1200 1500 1800

Velocity [km/h]

Time [s]

Driving cycle 3

136

Table C-1: Characteristics of the driving cycles of the diesel catering lift truck

Route

Distance

[km]

Duration

[s]

Max. speed

[km/h]

Average

speed

[km/h]

Stop time

[s]

Driving cycle 1

5.06

665

90.74

28.91

Driving cycle 2

9.11

1,344

109.71

24.72

Driving cycle 3

14.56

1,849

48.84

28.78

As can be seen in Figure C-1, high speeds of up to 110 km/h were measured several times,

which unreasonably exceed the maximum speed of 30 km/h on the airport apron. This is

especially noticeable in the first and second cycles. The measured values at these measuring

points are therefore considered to be measurement errors. Nevertheless, the average speed

remains relatively low, owing to the frequent stops in the speed profile.

137

D – Impact of Payload Variation on Energy Efficiency Analysis

Tables D-1 and D-2 indicate the impact of variation in payload weight on the efficiency of the

catering lift trucks under consideration.

Table D-1: Efficiency analysis of the diesel truck according to payload weight

Payload [kg]

𝜼𝑬 Driving system

[%]

𝜼𝑬 Lifting system

[%]

𝜼𝑬 Entire truck

[%]

32.76%

8.84%

31.85%

500

32.92%

9.27%

32.02%

1,000

33.08%

9.68%

32.17%

1,500

33.22%

10.06%

32.32%

2,000

33.36%

10.42%

32.46%

2,500

33.49%

10.76%

32.60%

3,000

33.62%

11.08%

32.73%

3,500

33.74%

11.39%

32.85%

4,000

33.85%

11.68%

32.97%

Table D-2: Efficiency analysis of the electric truck according to payload weight

Payload [kg]

𝜼𝑬 Driving system

[%]

𝜼𝑬 Lifting system

[%]

𝜼𝑬 Entire truck

[%]

103.07%

21.16%

99.24%

500

103.23%

22.07%

99.40%

1,000

103.39%

22.92%

99.56%

1,500

103.54%

23.72%

99.71%

2,000

103.68%

24.48%

99.85%

2,500

103.82%

25.19%

99.99%

3,000

103.96%

25.84%

100.13%

3,500

104.09%

26.48%

100.26%

4,500

104.21%

27.08%

100.38%

138

E – Acoustic Analyzer Used for the Noise Emission Measurements

Specifications

Frequency range

5 to 20 kHz

Sound pressure level range (SPL)

30 to 130 dB

Sensitivity

26.4 mV/Pa

Typical self-noise

21 dB

Temperature range

0 to +40 ℃

Temperature coefficient

< ±0.02 dB/℃

Influence of air pressure

-0.04 dB/kPa

Influence of air humidity (non-

condensing)

< ±0.4 dB

Figure E-1: XL2 acoustic analyzer [NTi Audio]

139

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List of Figures 
Figure  1-1:  Approach  for  evaluating  the  competitiveness  of  the  electrified  prototype  truck  versus 
conventional catering lift trucks ............................................................................................................. 20 
Figure 2-1: Reference diesel catering lift truck (Source: DOLL Fahrzeugbau GmbH).......................... 25 
Figure 2-2: Electrified catering lift truck (Source: DOLL Fahrzeugbau GmbH) ..................................... 26 
Figure 2-3: Typical work cycle of a catering lift truck ............................................................................ 28 
Figure 2-4: Google Earth mapping of the measurement driving cycles ................................................ 30 
Figure 2-5: Velocity profiles of the driving cycles of the electric catering lift truck ................................ 32 
Figure 2-6: Time distribution of the driving cycles according to the driving modes ............................... 34 
Figure 2-7: Hydraulic measurements of the lifting system (Source: DOLL Fahrzeugbau GmbH) ........ 36 
Figure 2-8: Time distribution according to the lifting functions .............................................................. 37 
Figure 3-1: Schematic structure of the simulation model of the diesel truck driving system ................ 39 
Figure 3-2: Schematic structure of the simulation model of the electric truck driving system .............. 39 
Figure 3-3: Simulation results of the power demand of the driving model ............................................ 45 
Figure 3-4: Simulation results of the consumption rate of the driving system ....................................... 47 
Figure 3-5: Simulation results of energy consumption of the driving system according to the driving 
modes .................................................................................................................................................... 48 
Figure 3-6: Simulation results of the battery SOC of the driving system of the electric truck ............... 49 
Figure 3-7: Simplified hydraulic circuit of the catering lift truck lifting system (Adapted from the original 
hydraulic circuit diagram provided by DOLL Fahrzeugbau GmbH) ...................................................... 51 
Figure 3-8: Schematic structure of the simulation model of the diesel truck lifting system ................... 52 
Figure 3-9: Schematic structure of the simulation model of the electric truck lifting system ................. 52 
Figure 3-10: Schematic representation of the combination of the 3-port/4-way directional control valve 
and double-acting cylinder actuator with variable definitions (for functions of the support legs, platform 
and platform tongue) ............................................................................................................................. 53 
Figure 3-11: Schematic representation of the combination of the 2-port/1-way directional control valve 
and single-acting telescopic cylinder actuator (for the function of lifting/lowering the container) ......... 56 

153 
 
Figure 3-12: Simulation results of the power demand of the lifting model ............................................ 58 
Figure 3-13: Simulation results of the battery SOC of the lifting system of the electric truck ............... 60 
Figure 4-1: Simulation results of the energy distribution of the driving system ..................................... 62 
Figure 4-2: Efficiency analysis of the driving system of the diesel truck ............................................... 63 
Figure 4-3: Efficiency analysis of the driving system of the electric truck ............................................. 63 
Figure 4-4: Simulation results of the energy distribution of the lifting system ....................................... 64 
Figure 4-5: Efficiency analysis of the lifting system of the diesel truck ................................................. 65 
Figure 4-6: Efficiency analysis of the lifting system of the electric truck ............................................... 66 
Figure 4-7: Vehicle efficiency analysis of the diesel truck ..................................................................... 67 
Figure 4-8: Vehicle efficiency analysis of the electric truck ................................................................... 67 
Figure 5-1: Composition of the TCO of the catering lift trucks .............................................................. 70 
Figure 5-2: Estimated development of diesel prices in Germany over time (Source: own assumptions 
based on literature review) .................................................................................................................... 76 
Figure 5-3: Estimated development of electricity prices in Germany over time (Source: own assumptions 
based on literature review) .................................................................................................................... 78 
Figure 5-4: Estimated development of battery prices in Germany over time (Source: own assumptions 
based on literature review) .................................................................................................................... 79 
Figure 5-5: Results of the TCO analysis for the defined operating scenarios ....................................... 86 
Figure 5-6: Development of the operation phase payouts over time .................................................... 88 
Figure 5-7: Development of the operation phase present values over time ......................................... 89 
Figure 5-8: Influence of diesel price variation on the TCO results ........................................................ 93 
Figure 5-9: Influence of electricity price variation on the TCO results................................................... 94 
Figure 5-10: Influence of battery price variation on the TCO results..................................................... 95 
Figure 5-11: Influence of diesel truck maintenance costs variation on the TCO results ....................... 95 
Figure 5-12: Influence of electric truck maintenance costs variation on the TCO results ..................... 96 
Figure 5-13: Influence of parameter variations on the TCO results ...................................................... 97 

154 
 
Figure 6-1: Development of GHG and PED factors per kWh electricity mix production ..................... 102 
Figure 6-2: Comparison of the overall emission impact according to the defined operating scenarios
 ............................................................................................................................................................. 105 
Figure 6-3: Comparison of the overall PED according to the defined operating scenarios ................ 105 
Figure 6-4: Influence of parameter variations on the LCA results ....................................................... 108 
Figure 7-1: Concept of the drive-by noise measurement setup .......................................................... 112 
Figure 7-2: Evaluation of the ambient noise ........................................................................................ 114 
Figure 7-3: Sound pressure level time course of the drive-by measurement ..................................... 115 
Figure 7-4: Evaluation of the drive-by measurement .......................................................................... 116 
Figure 7-5: Third-octave spectrum of the drive-by measurement ....................................................... 117 
Figure 7-6: Sound pressure level time course of the lifting measurement .......................................... 117 
Figure 7-7: Sound pressure level time course of the lowering measurement ..................................... 118 
Figure 7-8: Evaluation of the lifting system measurement .................................................................. 119 
Figure 7-9: Third-octave spectrum of the lifting measurement ............................................................ 120 
Figure 7-10: Third-octave spectrum of the lowering measurement ..................................................... 120 
Figure 8-1: Pressure losses in the lifting system of the diesel truck ................................................... 122 
Figure 8-2: Pressure losses in the lifting system of the electric truck ................................................. 122 
Figure 8-3: Energy distribution of the lifting/lowering process ............................................................. 124 
Figure B-1: Filtering zero-speed drifts ................................................................................................. 132 
Figure B-2: Filtering signal gaps .......................................................................................................... 133 
Figure B-3: Signal smoothing .............................................................................................................. 133 
Figure B-4: Signal processing of the driving cycle 1 ........................................................................... 134 
Figure C-1: Velocity profiles of the driving cycles of the diesel catering lift truck ................................ 135 
Figure E-1: XL2 acoustic analyzer [NTi Audio] .................................................................................... 138 

155 
 
List of Tables  
Table 2-1: Characteristics of the driving cycles of the electric catering lift truck ................................... 33 
Table 2-2: Definition of the driving modes ............................................................................................. 33 
Table 2-3: Characteristics of the defined work cycle of the lifting system based on measurement data
 ............................................................................................................................................................... 36 
Table 3-1: Key parameters of the driving simulation model .................................................................. 44 
Table 3-2: Simulation results of the driving model ................................................................................ 46 
Table 3-3: Energy saving by recuperation in the electric truck ............................................................. 50 
Table 3-4: Simulation results of the lifting model ................................................................................... 59 
Table 4-1: Comparison of the overall energy efficiency of the trucks for the three work cycles ........... 68 
Table 5-1: Key data of the defined daily operating scenarios of the catering lift trucks examined ....... 73 
Table 5-2: Cost parameters of the TCO analysis .................................................................................. 85 
Table 5-3: Basic comparison of the TCO of the two trucks ................................................................... 90 
Table 5-4: Comparison of the TCO of the two trucks when government subsidies are considered ..... 91 
Table  5-5:  Comparison  of  the  TCO  of  the  two  trucks  based  on  halving  the  battery  capacity  and 
eliminating the charging station ............................................................................................................. 92 
Table 6-1: GHG and PED factors per liter of diesel (WTW) ................................................................ 101 
Table 6-2: GHG and  PED factors for  battery  manufacturing and battery recycling using  the current 
German electricity mix ......................................................................................................................... 103 
Table 6-3: GHG and PED factors when using 100% renewable energy............................................. 104 
Table 7-1: Parameters used for evaluating measured noise emissions ............................................. 111 
Table 8-1: Influence of variation in pressure losses on energy efficiency of the diesel truck ............. 123 
Table 8-2: Influence of variation in pressure losses on energy efficiency of the electric truck ........... 123 
Table 8-3: Influence of potential energy recovery on energy efficiency of the electric truck .............. 125 
Table  A-1:  Technical  specifications  of  the  catering  lift  truck  X-Cat  M  from  DOLL  (Source:  DOLL 
Fahrzeugbau GmbH) ........................................................................................................................... 130 

156

Table C-1: Characteristics of the driving cycles of the diesel catering lift truck .................................. 136

Table D-1: Efficiency analysis of the diesel truck according to payload weight .................................. 137

Table D-2: Efficiency analysis of the electric truck according to payload weight ................................ 137

157

Own Publications

Partial results of this dissertation have already been published in some various publications

have emerged from this work, which are listed below:

Wdaah, L.; Müller, S., (2016). “Efficiency Analysis of an Electrification Concept for a Catering

Truck”, 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC

Asia-Pacific), Busan, South Korea, pp. 837-842.

Wdaah, L.; Müller, S., (2017). “Catering Truck of the Future — Efficiency Increase by full

Electrification”, 2017 2nd IEEE International Conference on Intelligent Transportation

Engineering (ICITE), Singapore, pp. 328-334.

Wdaah, L.; Müller, S., (2024). “Recovery of Potential Energy in the Lifting System of a Battery

Electric Catering Lift Truck”, submitted to: International Journal of Powertrains.

Wdaah, L.; Müller, S., (2018). “eLift-Cateringhubwagen der Zukunft”, Endbericht der TU

Berlin, Berlin.

158

Supervised Student Work

The following student theses were written as part of this work and supervised by me in the

context of my work as an assistant researcher in the Department of Automotive Engineering

at the Technical University of Berlin. Some of the topics are partial aspects of this dissertation

and have therefore been included but not referred to as references.

Bachelor theses and student research projects:

• Ghazal Safarini, Mohamed (2017): Potenziale zur Minderung des Energieverbrauchs

eines Catering-Fahrzeugs

• Hussain, Suleman (2018): Entwicklung und Durchführung eines Messkonzepts zur

Ermittlung von Lärmemission eines Catering-Fahrzeugs

• Nguyen, Phuong Anh (2015): Lebenszyklusanalyse als Entscheidungshilfe bei

Elektrifizierung eines Catering-Hubwagens

• Polat, Kemal (2018): Alternative Antriebskonzepte für ein Catering Hubfahrzeug -

Wirtschaftlichkeit und Leistungsvergleich

• Stulberg, Alex (2018): Potential zur Energierückgewinnung in einem elektrifizierten

Catering Hubwagen.

Master theses:

• Al-Sewari, Mohammed (2014): Design and Analysis of Linear Electrical Drives for a

Lift-Vehicle

• Bogdan, Niko (2016): Potential zur Elektrifizierung eines Sondernutzfahrzeuges

• Fang, Zhenjie (2106): Entwicklung eines Energierückgewinnungssystems für einen

Catering-Hubwagen

• Li, Wanjun (2106): Entwicklung und Anwendung einer Methodik zur Effizienzbewertung

eines Hubfahrzeugs