Analysis of the Energy Consumption of the Powertrain and the Auxiliary Systems for Battery-Electric Trucks

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IN

DEGREE PROJECT VEHICLE ENGINEERING, SECOND CYCLE, 30 CREDITS

STOCKHOLM SWEDEN 2020,

Analysis of the Energy

Consumption of the Powertrain and the Auxiliary Systems for Battery-Electric Trucks

GUANQIAO SONG

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES

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Author

Guanqiao SONG <guason@kth.se>

Master Programme in Vehicle Engineering KTH Royal Institute of Technology

Place for Project

Scania, Södertälje, Sweden

Date for Presentation

September 18, 2020

Examiner

Assoc. Prof. Jenny JERRELIND KTH Royal Institute of Technology

Supervisor

Assoc. Prof. Jenny JERRELIND KTH Royal Institute of Technology Dr. Antonius KIES

Scania CV AB Anders JENSEN Scania CV AB

English Title

Analysis of the energy consumption of the powertrain and the auxiliary systems for battery-electric trucks

Svensk Titel

Analys av energiförbrukningen i drivlinan samt för hjälpsystemen för batterielektriska lastbilar

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Abstract

The electrification of the truck is crucial to meet the strategic vision of the European Union (EU) to contribute to net-zero greenhouse gas emissions for all sectors of the economy and society. The battery-electric truck is very efficient to reduce the emissions and has also a lower Total Cost of Ownership (TCO) compared to diesel trucks. Thus, the energy consumption of the battery-electric truck needs to be analysed in detail, and the differences in the conventional powertrain, recuperation by regenerative braking during driving and charging during standing, need to be considered.

This master thesis aims to analyse the energy consumption of the battery-electric truck during driving and standing charging. For driving cycle simulation the Vehicle Energy Consumption calculation TOol (VECTO) and MATLAB are used. Different variations, such as payload, rolling resistance, air drag, and Power Take Off (PTO), are considered in the driving cycle simulation. The driving cycle simulation is verified by calculating the energy balance and compared with the on-road test results. For the standing charging simulation, MATLAB is used to analyse the charging loss with different battery packs and charging speeds. The results are shown with the Sankey diagram and other illustrative tools.

Seen from the simulation results, the usable energy of the battery pack is enough for the truck to complete the designed driving cycle. The main loss in the powertrain is the Power Electronic Converter (PEC) and the electric machine. To increase the range and reduce energy loss, using a higher efficiency PEC and electric machine is an efficient method. For the charging simulation, the current Combined Charging System (CCS) standard charging station can charge the battery-electric truck with adequate voltage and reasonable charging time. The main loss during the charging comes from the charging station.

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Keywords

Battery-electric truck, Energy consumption analysis, Battery loss, PT1 battery model, Power Take Off, Sankey diagram, Direct-Current charging

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Sammanfattning

Elektrificering av lastbilen är avgörande för att uppfylla Europeiska Unionens (EUs) strategiska vision att bidra till nettonollutsläpp av växthusgaser för alla sektorer i samhället. Den batterielektriska lastbilen är väldigt effektiv för att reducera utsläppen och är också mer ekonomisk med en lägre Total Cost of Ownership (TCO) jämfört med diesel lastbilar. Således behöver energiförbrukningen för den batterielektriska lastbilen analyseras i detalj, och skillnaderna i den konventionella drivlinan, återhämtning genom regenerativ bromsning under körning och laddning, måste övervägas.

Detta examensarbete syftar till att analysera energiförbrukningen för den batterielektriska lastbilen under körning och laddning. För körcykelsimuleringar används the Vehicle Energy Consumption calculation TOol (VECTO) och MATLAB.

Olika variationer, såsom nyttolast, rullmotstånd, luftmotstånd och Power Take Off (PTO), beaktas i körcykelsimuleringen. Körcykelsimuleringen verifieras genom att beräkna energibalansen som jämförs med experimentella testresultat utförda på väg.

För laddningssimuleringen används MATLAB för att analysera laddningsförlusten med olika batteripaket och laddningshastigheter. Resultaten visas med Sankey diagram och andra illustrativa verktyg.

Simuleringsresultaten visar att batteripaketets användbara energi är tillräckligt för att lastbilen ska kunna slutföra den planerade körcykeln. Den största förlusten i drivlinan är kopplat till the Power Electronic Converter (PEC) och den elektriska maskinen. För att öka räckvidden och minska energiförlusten är det ett effektivt sätt att en använda PEC och en elektrisk maskin med högre effektivitet. För laddningssimuleringen kan den nuvarande stationen med Combined Charging System (CCS) standard ladda batteriladdaren med tillräcklig spänning och med rimlig laddningstid. Huvudförlusten under laddningen kommer från laddstationen.

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Nyckelord

Batterielektrisk lastbil, Energiförbrukningsanalys, Batteriförlust, PT1 batterimodell, Power Take Off, Sankey diagram, Direct-Current charging

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Acknowledgements

This master thesis was carried out at Energy Economy and Sustainability, YDMC, Scania CV AB.

First of all, I am very grateful to my supervisors, Antonius Kies and Anders Jensen at Scania YDMC. During the whole project, they gave me tremendous valuable technical support for the truck simulation. Every problem I met in the project was solved with their abundant experience and knowledge. They kindly and patiently taught and guided me. Even during the vacation, we still had meetings to discuss and solve the issues. After working 4 months with my supervisors, I have learned a lot about truck energy consumption analysis. Without their help, it would have been really hard to finish this report. I hope we can meet again in Sweden or China.

Secondly, thanks to the experts at Scania. The battery expert, Johan Lindström at Scania Energy Storage, NEBE, with his comprehensive expertise and knowledge with the battery. Although he is very busy with daily work, he still took time to check my battery loss simulation and gave me a lot of help with the charging simulation. We had several meetings, even in the early morning. I am really thankful for his help. Thanks also to the Ola Hall at Scania Fluid Mechanics and Temperature Management, RTG.

His equations has been really helpful for the cooling system simulation.

Thirdly, I would like to express my sincere appreciation to my supervisor Jenny Jerrelind at KTH. During the whole project, she continuously closely followed my process. Moreover, she gave me detailed suggestions for my report. For my study at KTH, Jenny supported me during the courses and the thesis.

Lastly, I would like to thank my friends and family for all support and encouragement throughout the project and also my study at KTH.

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Acronyms

AC Alternating Current

AMS Air Management System

AMT Automated Manual Transmission

BOL Beginning Of Life

CC Constant Current

CCS Combined Charging System

CEU Central Electric Unit

CoP Coefficient of Performance

CV Constant Voltage

DC Direct Current

EU European Union

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EV Electric Vehicle

HEPS Hydraulic Electric Power Steering

HPC High Power Charging

DCIR Direct Current Internal Resistance

MCS Megawatt Charging System

OCV Open Current Voltage

PDU Power Distribution Unit

PEC Power Electronic Converter

PTO Power Take Off

SOC State of Charge

SOH State of Health

TCO Total Cost of Ownership

VECTO Vehicle Energy Consumption calculation TOol

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Chapter 1 Introduction

The Paris Agreement sets a long-term goal to mitigate global warming. To achieve the target of the Paris Agreement, the European Union (EU) has a strategic vision to contribute to net-zero greenhouse gas emissions for all sectors of the economy and society. According to the regulations of the EU, the greenhouse gas emissions from the road transport sector should be reduced by 30% by 2030 compared to 2005 [1].

Furthermore, the air pollutants from transport emissions have considerable harm to human health and the environment, which needs to be reduced immediately.

For the whole road transport greenhouse gas emissions, about 25% of the emission comes from heavy-duty vehicles, which include trucks, buses, and coaches [2].

Comparing with other technologies to reduce the greenhouse gas emission for road freight, such as fuel cells and Power-to-Liquid fuels, using battery-electric trucks is the most energy-efficient method [3]. Therefore, battery-electric trucks play an important role in reducing greenhouse gas emissions.

Besides reducing the greenhouse gas emission, the battery-electric truck also has a lower Total Cost of Ownership (TCO) compared with the diesel truck. The TCO contains vehicle cost, personal wages, fuel or electricity cost, maintenance and repair costs, insurance cost, and road use charges. For the battery-electric truck, the vehicle purchase cost is 1.5 to 4 times higher than the diesel truck at present depending on vehicle classes. However, the other costs, such as fuel or electricity cost, road toll charges, maintenance and repair costs, are much lower than the diesel truck [4, 5].

Based on the first five years long haul heavy duty truck TCO analysis, the operating cost of the diesel truck is between €1.0 and €1.1 per km, and the 480 km range battery-

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CHAPTER 1. INTRODUCTION

electric truck is slightly less than €1 per km [4]. For the light distribution truck, the operation cost of the battery-electric truck is 10% less than the diesel truck with mass production. Furthermore, the battery-electric truck needs less mileage to reach cost- parity comparing with the diesel truck with small-scale series production and mass production [5].

1.1 Company Presentation

Scania is a leading provider for commercial vehicles, including trucks and buses for heavy transport applications. It is part of TRATON GROUP, which includes Scania, MAN, and Volkswagen Caminhões e Ônibus. In 2019, the company had 51,000 employees in about 100 countries. The activities of research and development are mainly concentrated in Sweden. In the 2019 finical year, the company net sales reached a record level, 152,419 million SEK. In 2019, Scania delivered 91,680 trucks, 7,777 buses and coaches [6].

Scania contributes to decarbonising the heavy transport sector with different new technologies. The plug-in hybrid delivery trucks and hydrogen fuel cell trucks from Scania are already running on the road in Sweden and Norway. Scania is also doing researches to test and evaluate the electrified roads in Europe, which can be a new solution for zero-carbon long-haul heavy transport [6]. Scania has already deployed battery-electric trucks for city distribution in Oslo, Norway in 2020, see Figure 1.1.1 [7]. In the middle of the 2020s, the Scania battery-electric truck for long-haul heavy transport will be released to the market.

Figure 1.1.1: Scania battery-electric truck [7]

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1.2 Aim

The upcoming electrification of the truck powertrain in the 2020s means, that one main assembly group of the truck will be completely redesigned. This leads to the demand to analyse the energy consumption of the battery-electric truck in detail: air drag, rolling resistance, brake loss, powertrain losses, and auxiliary systems. Also, the differences in the conventional powertrain need to be considered: recuperation by regenerative braking during driving and charging during standing.

The aim of this master thesis is to analyse the energy consumption of four different battery-electric trucks: 2-axle delivery truck, 3-axle garbage truck, 5-axle tractor trailer, and 8-axle combi. The truck models are shown in Figure 1.2.1. At this moment, the Scania battery-electric truck is only at the early concept stage and not on the market yet. The energy consumption of different driving cycles and charging will be analysed and discussed. For the analysis of driving cycles, all the losses, from the battery to wheel, will be analysed. The results of the energy consumption for each component in the powertrain and the auxiliary systems will be visualised by the Sankey diagram.

For the garbage truck, an extra energy consumer, the Power Take Off (PTO), will be considered for garbage collection. Different influence factors to energy consumption in the driving cycles, such as payload, rolling resistance, and air drag, will also be discussed. In the charging analysis, all the losses for different battery packs, from the electric grid to battery, will be analysed with different charging speeds.

(a) 2-axle delivery truck (b) 3-axle garbage truck

(c) 5-axle tractor trailer (d) 8-axle combi Figure 1.2.1: Different truck type model [8]

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1.3 Procedure

In order to achieve the goal of the master thesis, the whole project can be separated into the following steps:

(1) Decide the structure and components of different battery-electric trucks.

(2) Literature review on the auxiliary systems energy consumption, the Power Take Off (PTO), the battery loss, and the efficiency of the components.

(3) Build the truck models and run the mechanical simulations with the Vehicle Energy Consumption calculation TOol (VECTO) [9]. Change the payload, rolling resistance, and air drag for different simulations.

(4) Run the electrical simulations with MATLAB based on the results from the VECTO and extract the energy consumption for different driving cycles.

(5) Run the simulation in MATLAB to analyse the charging losses with different charging speeds.

(6) Use the Sankey diagram and other illustrative tools to show and analyse the results.

1.4 Limitation

At this moment, the Scania battery-electric truck is still under the test. Thus, there is no on-road test result to be compared to verify the simulation results. The loss map, air drag, and rolling resistance coefficients used in the simulation are not the final values, but the generic data and best guesses from Scania, which will influence the accuracy of the simulation. For the battery analysis, the battery properties are based on the commercially available cell data in 2020. However, some of the trucks in the simulation will be released to the market in the mid-2020s. Thus, there will be some differences in future simulations due to the battery properties changes. The energy consumption of the auxiliary systems and the PTO is estimated by Scania experts. It will also influence the simulation accuracy, especially for the 3-axle garbage truck. Nevertheless, the result permits a basic assessment of energy consumption and losses.

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1.5 Outline

Chapter 2: The structure of the battery-electric truck is shown. The energy consumption, efficiency, and loss for each system and component are introduced.

Chapter 3: The parameters of trucks are shown here, such as the truck mass, electric machine parameter, tyre size, and gear ratios.

Chapter 4: The simulation processes for the project are described with all the variations in the simulations. The process of the model building with VECTO and the calculation method in the MATLAB are shown here.

Chapter 5: The results of the simulations are shown here. Based on the results, the influence of each variation on energy consumption is discussed as well as sources of error and the accuracy of the simulations.

Chapter 6: The whole project is concluded with a summary. Possible future work that has been identified is presented.

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Chapter 2 Background

In this chapter, the structure of the battery-electric truck is presented. Then, the battery model and the battery loss analysis method are explained. An introduction of the battery charging process and the charging loss are given. Next, each component in the powertrain with its function and efficiency is introduced. Finally, the energy consumption of auxiliary systems and the PTO are discussed.

2.1 Battery-Electric Truck Structure

In the simulation, the structure of all the investigated battery-electric trucks is the same, which is shown in Figure 2.1.1. In this figure, the electrical energy is represented by blue blocks and lines, and the mechanical energy is represented by red blocks and lines. The solid line means energy flow and the dashed line means energy loss.

For the Scania battery-electric truck, it can only be charged in Direct Current (DC).

Thus, the electrical energy will be charged to the battery by DC chargers from the electric grid. During the driving cycles, the electrical energy in the battery will come to the panel. The panel can distribute the energy in different ways: to the electric machine, auxiliary system (Aux. in the figure), or Power Take Off (PTO). The PTO is only used in the 3-axle garbage truck for garbage collection. After the panel, the electrical energy will come to the Power Electronic Converter (PEC), which can supply the electrical energy to the electric machine during driving and can charge the battery during braking. The details will be introduced later.

The E EM block in the figure means the electrical energy at the electric machine, and

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CHAPTER 2. BACKGROUND

Figure 2.1.1: Structure of Battery-Electric Truck

the electrical energy will convert to mechanical energy during driving or in the opposite way during braking. This is the reason that the electric machine has both electrical and mechanical energy loss. When the electric machine works as a motor, it will be called the motor case. When it works as a generator, it will be called the generator case. The W EM map in the figure means the mechanical work at the electric machine. It will go into two different ways. One is the motor inertia. It is considered to be a loss in the motor case, but it is a gain for the generator case. The other mechanical energy will come to the electric machine shaft (EM shaft in the figure), it is similar to the crankshaft in a diesel truck.

After this part, the structure of the battery-electrical truck powertrain is similar to a normal diesel truck. The electric machine shaft will connect to the gearbox without a clutch. The gearbox and axle gear are connected by the propshaft. The hub is connected with axle gear by the driveshaft. At the wheels, the vehicle inertia and gradient energy can be recovered and charge the battery. The energy used to overcome the rolling resistance and air drag is always a loss.

As shown in Figure 2.1.1, the loss is unavoidable at every component and system in the truck. Each loss in the figure will be analysed in the project. After the simulation, the sum of all the losses and the energy consumption by the auxiliary system and the PTO must be equal to the energy consumption in the battery. The powertrain includes all components from the battery to the wheel hubs [10]. According to this definition, the auxiliary systems and the PTO are not part of the powertrain, but additional energy consumers.

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2.2 Battery

The key component in the battery-electric truck powertrain is the battery. For the Electric Vehicle (EV), the Li-ion battery cell is the most commonly used due to its high energy density, light weight, long lifespan, and thermal stability [11]. There are three different cell designs used for EV, the cylinder cell, prismatic cell, and pouch cell, which are shown in Figure 2.2.1.

(a) Cylinder cell (b) Prismatic cell

(c) Pouch cell

Figure 2.2.1: Different cell designs [12]

The properties of the three cell designs are various. For the cylinder cell, it has low packaging density due to space cavities, but the cost per kWh is the lowest for the three cells. The prismatic cell has a high packing efficiency, but the cost is also higher [13].

For the pouch cell, there is no hard casing instead it has a cover of the polymer-metal material. Compare with the other two cell designs with the hard-case, the pouch cell can more easily lead to an accident when thermal runaway happens [12]. The weight of the pouch cell is lower with the highest packing efficiency [13].

In the battery pack, several battery cells are connected in series as a string. The cell number in the string will determine the voltage of the whole battery pack. Then, the strings will be connected in parallel in the pack. The number of parallel coupled strings will determine the pack capacity.

The battery property is significant for energy consumption analysis during driving cycles and charging. However, this master thesis focuses on energy consumption

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analysis at the vehicle level. The battery part is not a major part of the whole report.

Thus, the theory introduction of the battery will only focus on the knowledge used in the simulation.

2.2.1 State of Charge

The State of Charge (SOC) is a key parameter for the battery simulation. It is defined by the ratio of the remaining capacity to the cell maximum capacity, which can be expressed [14]:

SOC = Q_re

Q_cell (2.1)

Where:

• Q_reis the cell remaining capacity

• Q_cellis the cell maximum capacity

In the simulation, the battery charging or discharging will be analysed based on the energy. Thus, in this report, the SOC will be calculated by the ratio of the pack remaining energy E_reto the battery pack installed energy E_pack:

SOC = E_re

E_pack (2.2)

All the SOC is based on the installed energy. To maintain the battery State of Health (SOH), the minimum and maximum value of the E_reare 15% and 80% of the E_pack. In other words, the SOC will only be varied between 15% to 80%. The calculation of the E_pack will be mentioned later.

2.2.2 Direct Current Internal Resistance of Battery Cell

When charging or discharging the battery, the current in the circuit is in DC. Thus, the Direct Current Internal Resistance (DCIR) is essential for the energy consumption analysis to calculate the battery loss during driving cycles and charging. For a Li-ion battery cell, the DCIR is not a constant value. It will change at different temperature, SOC, SOH, and charge/discharge current. The key influence factor is the SOC [15].

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In order to determine the DCIR of a battery cell, the Thevenin equivalent circuit model will be used, which is the most commonly used model for a Li-ion battery [16]. The model is shown in Figure 2.2.2. The Open Current Voltage (OCV) is the voltage measured as the circuit current is zero. U_cell is the battery terminal voltage, and I_cell is the current in the battery cell [15].

Figure 2.2.2: Thevenin equivalent circuit model [15, 16]

The internal resistance of the battery is not a simple ohmic resistance. It includes the ohmic resistance and polarized resistance [16]. The R_S is the ohmic resistance, which depends on the material of the battery, such as electrode and electrolyte, and changes with the SOC. The polarized resistance is caused by the battery electrochemical reaction during charging or discharging. As the current in the cell changes, the polarized resistance will also change. Thus, the polarization resistance R_P and polarization capacitance C_P connected in parallel can represent the polarized resistance [16, 17]. The polarized voltage u_P is used to determine the polarized resistance. It is calculated by the Kirchhoff’s voltage law with the PT1 method, the equation is shown [15]:

u_P = I_cell· RP + (u_P(0)− Icell· RP)· exp( −t

R_PC_P) (2.3)

Where:

• u_P(0)is the initial voltage of C_P

• t is time length that the current keeps constant

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The voltage drop due to the DCIR ∆V_DCIRis calculated as [15]:

∆V_DCIR= I_cell· RS+ u_P (2.4)

Using the Ohimic’s Law, the DCIR of the battery cell DCIR_cell is calculated as:

DCIR_cell = ∆V_DCIR

I_cell (2.5)

Combining Equations 2.3, 2.4, and 2.5, the final equation to calculate the DCIR_cellis:

DCIR_cell = Icell· RS+ Icell· RP + (uP(0)− Icell· RP)· exp(_R_P^−t_C_P)

I_cell (2.6)

As mentioned before, the main influence factor of the DCIR is the SOC. Thus, the parameters in the Equation 2.6, R_S, R_P, and C_P, will vary at different SOC [15]. An example for the DCIR calculation with PT1 method at a constant SOC during charging is shown Figure 2.2.3.

Figure 2.2.3: PT1 and reference DCIR

The DCIR of the battery cell at different SOC and charging/discharging duration is the reference value extracted from the commercially available cell datasheet, which is shown as the solid line in Figure 2.2.3. In the simulation, the u_P(0)is set to zero. The I_cellis set to be the current at the 1-C rate for charging/discharging. Every time when the current just changes, the DCIR_cell can be estimated as the value of R_S [16]. In

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this example, R_S is equal to the 1-second charge DCIR reference value, which is 4.4 mΩ. When the battery is charged or discharged under a constant current for a long time, the measured value of DCIR_cellwill be constant and equal to the sum of R_S and R_P [17]. As for the example, the reference continuous charging DCIR_cell is 8.8 mΩ.

Thus, the R_P can be calculated: 8.8-4.4=4.4 mΩ. With a settled value of R_Sand R_P at certain SOC, the value of the C_P can be adjusted based on the measured value of DCIR and Equation 2.6. The DCIR_cell with the PT1 method is shown as the dashed line in Figure 2.2.3.

2.2.3 Voltage of Battery Cell

For a Li-ion cell, the Open Current Voltage (OCV) will change at different SOC in a functional relationship. The relationship between the SOC and OCV for a typical Li- ion cell is shown in Figure 2.2.4 [16].

Figure 2.2.4: OCV-SOC curve for Li-ion cell [16]

The battery cell terminal voltage depends on the OCV of the cell U_cell,OCV, cell current I_cell, and cell internal resistance DCIR_cell. At different SOC, the terminal voltage U_cell of the battery cell is calculated as [18]:

U_cell = U_cell,OCV − DCIRcell· Icell (2.7)

In Equation 2.7, the battery cell current I_cellis positive during discharging and negative during charging. For a example Li-ion cell, the curve of OCV, U_cell in 0.05 C-rate for charging and discharging at different SOC are shown in Figure 2.2.5 [18].

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Figure 2.2.5: Curve of OCV and terminal voltages versus SOC [18]

2.2.4 Voltage, Resistance and Current of Battery Pack

In order to analyse the properties of a battery pack, a battery pack model from Scania will be used, which is shown in Figure 2.2.6.

Figure 2.2.6: Scania battery pack model [19]

In a battery pack, numbers of cells are connected in series to form a string, and several strings are connected in parallel inside one pack. In Figure 2.2.6, the battery pack OCV is U_pack,OCV, the pack internal resistance is DCIR_pack, the pack current is I_pack, the terminal voltage of the pack is U_pack, and the load power to the pack is P_pack,load.

The battery pack OCV U_pack,OCV and the pack nominal voltage U_pack,nomare calculated

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as:

U_pack,OCV = U_cell,OCV · Nseries

Upack,nom = Ucell,nom· Nseries

(2.8)

Where:

• Nseriesis the number of serial coupled cells

• U_cell,nomis the cell nominal voltage

The internal resistance of a battery pack DCIR_pack is calculated as:

DCIR_pack = (DCIR_cell· Nseries+ R_cable)/N_parallel (2.9)

Where:

• R_cable is the resistance in string caused by wiring, which is 10 mΩ estimated by Scania

• N_parallelis the number of parallel coupled strings The battery pack terminal voltage U_packis calculated as:

U_pack = U_pack,OCV − DCIRpack · Ipack (2.10)

The load power at clamps of the battery pack P_pack,load is calculated as:

P_pack,load = U_pack· Ipack

= (U_pack,OCV − DCIRpack· Ipack)· Ipack

(2.11)

In the simulation, during discharging, the P_pack,load is defined to be positive. During charging, the P_pack,load is defined to be negative.

By solving the Equation 2.11, the battery pack current I_pack is calculated as:

I_pack = U_pack,OCV 2· DCIRpack

− Ã(

U_pack,OCV 2· DCIRpack

)2

− P_pack,load

DCIR_pack (2.12)

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The relationship between the cell current I_cell and I_packis calculated as:

I_pack = I_cell· Nparallel (2.13)

2.2.5 Power and Energy of Battery Pack

With the pack current I_pack and the pack internal resistance DCIR_pack, the loss power of the battery pack P_pack,lossis calculated as:

P_pack,loss = I_pack² · DCIRpack (2.14)

The overall power of the battery pack P_pack at the active material change of the SOC during charging and discharging is calculated as:

P_pack = P_pack,load+ P_pack,loss (2.15)

The pack capacity Q_pack and pack installed energy E_pack are calculated as [20]:

Q_pack = N_parallel· Qcell

E_pack = Q_pack · Upack,nom· (2.16)

2.3 Battery Direct-Current Charging

For the Scania battery-electric truck, it will only use the DC charging. For different trucks, there are two charging modes: depot charge and opportunity charge. The charging speed of the depot charge is slow and the charger is cheaper than the opportunity charger. When choosing the charger for the depot charge, the principle is that the battery can be charged from 15% to 80% SOC overnight, stand for 1-shift operation. For the opportunity charge, the charging speed is limited by the battery pack design or the charging station power. The charger will choose the charging station with the maximum power at the present.

Currently, there are three main DC charging standards: CHAdeMO, Combined Charging System (CCS), and GB/T [21]. This report will follow the latest CCS standard.

The standard defines the range of charging voltage, current, and power. Until now,

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the maximum charging power for a DC CCS charging station is 350 kW [22]. For commercial vehicles, a new high power charging standard, named Megawatt Charging System (MCS), is still under development. The target charging power for this standard will be more than 2 MW [23].

2.3.1 Charging Strategy

The commonly used charging strategy for a Li-ion battery is the Constant Current (CC)- Constant Voltage (CV) strategy. For a battery cell, the curves of the cell terminal voltage and current during the CC-CV charging are shown in Figure 2.3.1.

Figure 2.3.1: Cell terminal voltage and current in CC-CV charging [24]

As the figure shows, the CC-CV charging strategy can be separated into two phases.

At the beginning of charging, the battery will be charged under constant current. The current at this stage depends on the charging current limitation of the cell, which will be mentioned later. The cell terminal voltage U_cellis applied by the charging station. It is determined by the the cell OCV U_cell,OCV and current I_cell[25]. As mentioned before, the U_cell,OCV will change at different OCV and the cell current I_cellis negative during the charging. The Equation 2.7 can be used to determine the charging terminal voltage of the cell during CC phase: U_cell = U_cell,OCV − DCIRcell· Icell.

As the voltage reaches the predefined voltage, the next CV phase will start. At this phase, the cell terminal voltage applied by the charger will remain constant. The current will decrease until the battery SOC reaches the target value. According to

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Scania estimation, the applied voltage at CV phase will be around 5% to 10% lower than the upper cutoff voltage of the cell. The current at the CV phase will follow the charging current limitation of the cell [25].

During the charging, the cell charging current limitation is important. The battery current limits depend on the SOC, temperature, and time duration. The reason to limit the battery current is to prevent the battery from overheating. An example of the charging current limitation versus the SOC at different charging time duration is shown in Figure 2.3.2.

Figure 2.3.2: Charging current limitations [26]

At the early stage of the charging, the current limit is high for each time duration. When the battery SOC reaches a certain value, the maximum charging current will decrease.

Furthermore, different charging duration influences largely the current limitation.

In order to reduce the charging time, at the beginning of charging, the current can reach the value of the 30-second charging current limit. After that, the battery will charge with CC strategy, the current should be smaller than the value of the continuous charging current limit. In the simulation, when the maximum allowance charging current decreases, the charging will turn to the CV strategy. The current will decrease with the continuous charging current limit decreasing. The duration of the charging current in 30-second limit t₃₀is calculated as [26]:

t₃₀= 30

i_30s,limit

|Icell| (2.17)

Where:

• i_30s,limitis the 30-second charging current limit

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• |Icell| is the absolute value of the current in the battery

The charging current limitation is only considered when the battery is charged by the charger. For the on-board charging during driving cycles, the charging duration is often less than 30 seconds. Most of the time, the cell current is lower than the limitation under the simulation temperature. Only for a few seconds for the delivery truck, the charging current is higher than the limits, which is ignored.

2.3.2 Charging Power and Losses

During the charging, the power need for charging the battery pack can be calculated with the Equation 2.11: P_pack,load = U_pack · Ipack. The battery loss during the charging can be calculated with Equation 2.14: P_pack,loss= I_pack² ·DCIRpack. According to Scania, during the charging, there are still some components that need to be active. The power need of the active components during the charging is called idle power and it is estimated to 0.8 kW.

Thus, the power output of the charger P_charger and the battery charged power P_batt,in can be calculated as:

P_charger = P_pack,load+ P_idle P_batt,in= P_pack,load − Ppack,loss

(2.18)

When charging the vehicle, the charger will get the energy from the electric grid and convert the current to DC. The efficiency of the charger η_chargeris set to be 92% in the simulation [27–29]. The loss power of the charger Pcharger,lossand the power need from the electric grid P_grid are calculated as:

Pcharger,loss = P_charger· (1 − ηcharger) Pgrid = Pcharger+ Pcharger,loss

(2.19)

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2.4 Powertrain Components

In the first section of this chapter, the electric-truck structure is introduced and shown in Figure 2.1.1. There are different components between the battery and wheel hubs.

There are both the mechanical powertrain components: gearbox and axle gear, and the electrical components: panel, Power Electronic Converter (PEC), and electric machine.

The energy loss for each component is essential for energy consumption analysis.

2.4.1 Panel

After the battery, the first component in the powertrain is the panel. It is also called Power Distribution Unit (PDU), distribution board, or junction box. The Scania name is Central Electric Unit (CEU). In this report, it will be called the panel. The main function of the panel is to distribute the electrical energy to different components or systems [30]. The panel model used in the simulation is shown in Figure 2.4.1.

Figure 2.4.1: Panel application diagram

Besides distributing the energy, the panel can also provide short circuit protection, current leaking protection, and communication with the vehicle CAN-BUS. The panel consists of some simple electronic devices, wires, and connectors [31]. Thus, the

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efficiency of the panel η_panelis high and sets to be 99.5% in the simulation [6, 32].

In this thesis, the panel will distribute the energy to the auxiliary system, to the PTO for the garbage truck, and to the electric machine. All the electrical energy charged or discharged to the battery during the drive cycles will pass through the panel.

2.4.2 Power Electronic Converter (PEC) and Electric Machine

For the battery-electric truck, the electric machine generates torque. The PEC injects a current into the electric machine and make the rotor rotate. Ultimately, the vehicle will move [12]. The PEC is a key component that interfaces the electrical energy from the battery with the electric motor. The PEC can provide high quality, reliable, and efficient power for on-board charging or discharging [33].

The PEC is also called Integrated Power Electronics Interface (IPEI). In the vehicle, the voltage difference between the battery and the electric machine is smaller than 4 times. The DC/DC boost converter is used in PEC, which is a non-insulating converter with a high efficiency up to 99% [34, 35]. In the PEC, there is a bidirectional DC/DC boost converter connecting to the panel. The power absorbed by the battery or injected into the system is controlled by the DC/DC boost converter. On the other side of the DC/DC converter, there is a DC-Link connects the DC/Alternating Current (AC) source converter. The DC/AC converter will transfer the energy from the DC-link to the electric machine [36, 37]. The efficiency of the DC/AC converter is also high and even more than 99% [12]. The schematic diagram of the PEC is shown in Figure 2.4.2.

In the PEC, the DC/DC converter will allow the desired DC voltage level to power the electric machine in the motor case or the battery can be charged on-board in a proper voltage in the generator case. For the DC/AC converter, it can convert the DC power to a suitable AC power to operate the electric machine, and thereby works as an inverter.

On the contrary, it can provide a suitable DC power from the electric machine during on-board charging, and thereby works as a rectifier [33, 36].

In the simulation, the sum of the electrical power demand of the PEC and the electric machine can be interpolated from the Scania PEC & EM map with the torque and rotational speed of the electric machine. The sum of losses for these two components is the difference between electrical and mechanical energy. The efficiency of the PEC η_{P EC}is set to be 98% in the simulation [33, 36, 38]. Based on the efficiency, the energy

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Figure 2.4.2: PEC application diagram [37]

loss from the PEC and the electric machine can be split.

2.4.3 Mechanical Powertrain Components

In the electric machine, the electrical energy will transfer to the mechanical energy as a motor or in the opposite way as a generator. After the electric machine, all the energies are mechanical energy. The other components in the powertrain are the mechanical components and similar to a conventional truck.

Different from the common passenger EV, there is a multi-speed gearbox in the battery-electric truck. The reason for the gearbox is that the torque demand for the electric machine will vary a lot due to different load cases. The electric machine shaft connects the gearbox with the electric machine. The Scania battery-electric truck will use 2- or 4-speed Automated Manual Transmission (AMT). After the gearbox, there is an axle gear between the propshaft and driveshaft. Finally, the driveshaft will connect to the wheel hubs.

In the simulation, the rotational speed and power in the wheel hubs will be generated with the VECTO. With gear ratios for different components, the torque load and the rotational speed for each component can be calculated. With the torque and the rotational speed, the losses of the gearbox and the axle gear can be interpolated from the Scania loss maps.

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2.5 Auxiliary System

For the battery-electric truck, different auxiliary systems are working during the driving cycles. The power of the auxiliary system loads cannot be ignored for the energy consumption analysis. The energy consumption of the auxiliary systems will highly depend on the temperature, vehicle type, and technology selection [39]. In this thesis, the auxiliary system will only be estimated roughly for different truck types under one temperature. The system analysed in the thesis will include the steering system, pneumatic compressor, air conditioning, cooling system, and general electric systems.

The working voltage for the most auxiliary systems is 24 V. Thus, a DC/DC converter is needed to convert the higher DC input voltage (battery pack voltage) to 24 V. Due to the high voltage difference, an insulating DC/DC converter will be used, and the efficiency η_convis set to be 95% [35, 40].

2.5.1 Steering System

For the current electric commercial vehicles, the Hydraulic Electric Power Steering (HEPS) is the most commonly used [41]. In the HEPS, there is an electric motor to drive the hydraulic pump for assisting steering. The working voltage of the steering system is 24 V [39].

The standard mechanical power demand of the steering pump for different trucks and cycles are shown in Table 2.5.1. In Table 2.5.1, U is the pumping oil without steering pressure demand, F is the friction in the pump, B is the steer correction due to banking of the road or side wind, and S is the steer pump power demand due to cornering and manoeuvring.

Table 2.5.1: Standard mechanical power demand of steering pump [42]

Truck

Steering Power Consumption [W]

Long Haul Regional Delivery Urban Delivery Municipal Utility

U + F B S U + F B S U + F B S U + F B S

Delivery 510 100 0 490 40 40 430 40 50 430 30 50

Garbage 600 120 0 490 60 40 440 50 50 430 30 50

Tractor 600 120 0 540 90 40 N/A

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In Table 2.5.1, the 2-axle delivery truck, and 3-axle garbage truck are named as Delivery and Garbage. The 5-axle tractor trailer and 8-axle combi are named as Tractor. There are four different cycles shown in the table: long haul, regional delivery, urban delivery, and municipal utility [42].

The power consumption of the steering system needs to be calculated with extra scaling factors. For the HEPS, The factor of U and F , c_{U +F}, is zero. The factor of B, c_B, is 1.5/ηalt. The factor of S, c_S, is 1/η_alt. The η_alt is the alternator efficiency. For a diesel truck, the energy source of the electric motor in HEPS is the engine. Due to the energy conversion from the mechanical energy to the electrical energy, the η_alt is 0.7 for the diesel truck [42]. However, the steering electric motor in the battery-electric truck will power by the battery through the panel. The η_altis 1 for the battery-electric truck.

For each cycle, the power demand for the steering pump is the sum of U + F , B, and S with the factors (e.g. delivery truck with the regional delivery cycle, the power is 490× 0 + 40 × 1.5 + 40 × 1 = 100 W). The main driving cycle for different trucks is different. The steering pump power for the delivery truck will take the average value of regional delivery and urban delivery cycles (e.g. (100 + 110)/2 = 105 W). For the garbage truck, it will take the value of the municipal utility cycle. For tractor trailer and combi, it will take the average value of long haul and regional delivery cycles.

Furthermore, there will be an extra energy loss due to the voltage change from the battery voltage to 24 V. The final power consumption of the steering system at the panel needs to consider the converter efficiency η_conv, which is 95% (e.g. the final steering power for delivery truck is 105/0.95 = 111 W).

Therefore, the final electrical power and energy demands on the panel of the steering system for different trucks are shown in Table 2.5.2. For the energy calculation, the time and distance of each driving cycle are shown in Appendix B.1. The delivery truck is calculated based on the easy delivery cycle, medium delivery cycle, and heavy delivery cycle. The garbage truck is calculated based on the municipal utility cycle. The tractor and combi are calculated based on the regional delivery cycle and long haul cycle.

Table 2.5.2: Electric power and energy demand of the steer system at the panel

Truck Type Delivery Garbage Tractor Combi

Power [W] 111 100 187 187

Energy [kWh/km] 0.004 0.012 0.003 0.003

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2.5.2 Pneumatic Compressor

The pneumatic system is essential for commercial vehicles. The main components in the pneumatic system are the valves, tanks, the air dryer, and the air compressor. The pneumatic system in the battery-electric truck will power the service brake, release the parking brake, and power the suspension system [39].

For the Scania battery-electric truck, it will use the large air compressor with more than 500 cm³displacement. It will also have the Air Management System (AMS) to reduce the power consumption of the air compressor. The mechanical power demand of the pneumatic system with different technologies and cycles is shown in Table 2.5.3.

Table 2.5.3: Power demand of the pneumatic system [W] [42]

Tech.

Cycle

Long Haul Regional Delivery

Urban Delivery

Municipal Utility

Baseline 4300 3600 3500 3500

+mech. clutch -3500 -2800 -2800 -2800

+AMS -500 -300 -200 -200

Sum 300 500 500 500

As shown in Table 2.5.3, the baseline is the standard compressor power consumption of the cycle without any energy-saving technology. With different technologies, the power consumption of the compressor will reduce. The mechanical clutch is mounted between the compressor and the engine. When there is no power demand of the compressor, it will disconnect the engine and the idle loss is zero. However, for the battery-electric, the compressed air is produced by the electric compressor, and it will turn off when there is no power demand, which can also reduce the power consumption and is similar to the mechanical clutch. Thus, the power reduction by the mechanical clutch (mech. clutch in Table 2.5.3) will be included in the battery-electric air compressor. As mentioned before, the AMS will also be included for the battery- truck pneumatic system. The mechanical power demand of the pneumatic system of each cycle is shown in the last row of Table 2.5.3.

The cycle for the different trucks are chosen as discussed before. The delivery truck will take the average value of regional delivery and urban delivery cycles (e.g. (500 + 500)/2 = 500W). For the garbage truck, it will take the value of the municipal utility cycle. For tractor trailer and combi, it will take the average value of long haul and regional delivery cycles. For an EV, the pneumatic brake will be less than a diesel truck

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due to the regenerated braking power from the electric motor. It is estimated by Scania that the EV power demand of the pneumatic system is half of the diesel truck (e.g. for delivery truck is 500/2 = 250 W).

In Table 2.5.3, power is the mechanical power. For the air compressor, it will have its independent inverter. The electrical energy will directly come from the panel. The efficiency of the energy transfer is about 85% (e.g. for delivery truck is 250/0.85 = 294 W) [41, 43].

Thus, the final electrical power and energy demands on the panel of the pneumatic compressor for different trucks are shown in Table 2.5.4. The driving cycle details for energy calculation is the same as the calculation for the steering system. In the simulation, the impact of the payload on the pneumatic compressor power is neglected.

Table 2.5.4: Electric power demand of the pneumatic compressor at the panel

Power [W] 294 294 235 235

Energy [kWh/km] 0.010 0.035 0.003 0.003

2.5.3 Air Conditioning for Cooling

The power consumption of the air conditioning system highly depends on the ambient temperature. In this simulation, the mechanical power of the air conditioning system for cooling comes from the EU Commission Regulation [42]. The standard mechanical power of the air conditioning system is shown in Table 2.5.5.

Table 2.5.5: Mechanical power demand of the air conditioning [W] [42]

Truck

Cycle

Long Haul Regional Delivery

Urban Delivery

Municipal Utility

Delivery 350 200 150 300

Garbage 350 200 150 300

Tractor 350 200 N/A

The main components of the EV air conditioning system are the chiller, blowers, and fans. For the whole air conditioning system for cooling, the main power consumer is the compressor. The EV air conditioning system will use the electric compressor

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[44]. Thus, the efficiency of the whole air conditioning system can be regarded as the compressor efficiency, which is 90% [45]. The working voltage of the air conditioning system is 24 V [38, 39]. Thus, the converter efficiency η_convalso needs to be considered for calculating the sum electrical power at the panel.

The cycle for the different trucks is the same as the previous analysis. The example to calculate the delivery truck air conditioning mechanical power is (200 + 150)/2 = 175 W, and the electrical power at the panel is 175/0.95/0.90 = 205 W.

The final electrical power and energy demand on the panel of the air conditioning system is shown in Table 2.5.6.

Table 2.5.6: Electric power and energy demand of the air conditioning at the panel

Power [W] 205 351 322 322

Energy [kWh/km] 0.007 0.042 0.005 0.005

In the simulation, the heating is not needed based on the temperature. Thus, the air conditioning for heating is not considered in this report.

2.5.4 Cooling System Electrics

In the battery-electric truck, different components need to be cooled. During driving cycles, the heat will mainly be generated by the battery pack, the PEC, and the electric machine. In order to take away the heat, the chiller, the fan, and the coolant pump are needed. In the simulation, the power demand for the cooling system is based on the Scania preliminary assumption. For the different trucks and cycles, the equation is the same.

One of the components in the cooling system is the chiller, which will dissipate the coolant heat from the battery. It will work at 24 V. Thus, a converter is needed and the efficiency η_convis 95%. The electrical power of the chiller at the panel can be calculated with the Scania preliminary function:

Pchiller = P_pack,loss 2.5· ηconv

(2.20)

Where:

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• 2.5 is the estimated Coefficient of Performance (CoP) of the chiller by Scania Another component in the cooling system is the fan. The fan can take away the heat of the battery through the chiller, it will also cool the electric machine and PEC by air cooling. The energy of the fan will directly come from the panel. The function to calculate the fan power at the panel is:

P_{f an} = 0.003· (Pem,lossmap+ P_pack,loss) (2.21)

Where:

• P_em,lossmapis the loss power of the electric machine and PEC from the map

• 0.003 is an estimated factor by Scania

The final component to be discussed in the cooling system is the pump. The pump will cycle the coolant inside the battery pack and the chiller. The electrical power of the pump is estimated at 500 W by Scania. The pump will work at 24 V. Thus, the final electrical power consumption of the pump at the panel is: 500/0.95 = 526 W.

2.5.5 General Electric Systems

In the battery-electric truck, different electrically driven equipment will work during driving cycles, such as the infotainment system, lamps, wipers, and controllers. All these devices will work at 24 V for the commercial vehicle [38, 39]. The power demand of these components under different cycles is shown in Table 2.5.7.

Table 2.5.7: Electric power demand of electric system [42]

Cycle Long Haul Regional Delivery Urban Delivery Municipal Utility

Power [W] 1200 1000 1000 1000

The cycle for the different trucks is the same as the previous analysis. The example to calculate the delivery truck electric systems power is (1000 + 1000)/2 = 1000 W, and the electrical power at the panel is 1000/0.95 = 1053 W. The final electrical power and energy demand on the panel of the air conditioning system is shown in Table 2.5.8.

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Table 2.5.8: Electric power and energy demand of the electric systems at the panel

Power [W] 1053 1053 1158 1158

Energy [kWh/km] 0.035 0.127 0.017 0.017

2.5.6 Auxiliary System Summary

From the previous introduction, the power consumption of the auxiliary system in the simulation is shown clearly. Except for the chiller power P_chiller and fan power P_{f an}, all other power consumption will remain constant for one truck type. In Table 2.5.9, the summary of the auxiliary system power and energy demand for different trucks is shown. The P_chillerand P_{f an}are not including here.

Table 2.5.9: Auxiliary system power summary

System

Truck

Delivery Garbage Tractor Combi

Steering [W] 111 100 187 187

Pneumatic [W] 294 294 235 235

Air Con. [W] 205 351 322 322

Pump [W] 526 526 526 526

Electric [W] 1053 1053 1158 1158

Sum [kW] 2.189 2.324 2.428 2.428

Sum [kWh/km] 0.073 0.280 0.035 0.035

2.6 Power Take Off (PTO)

For the 3-axle garbage truck, the PTO will mount on the truck to convert the energy from the power source. It will provide the power to collect and compress garbage from the panel [46]. The structure and main components of the rear loader garbage truck with a translation compaction system are shown in Figure 2.6.1.

The garbage truck will collect the refuse with two main steps. The first step is to load the refuse into the hopper of the garbage truck. In this step, the container or dustbin will connect the truck container lock. Then a tipper will empty the refuse into the hopper.

In the next step, the refuse in the hopper will be packed in the body of the garbage truck [48]. The procedure of this step is more complex and shown in Figure 2.6.2.

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Figure 2.6.1: Rear loader garbage truck structure [47]

Figure 2.6.2: Refuse packing procedures [48]

In the first procedure, the sweep panel will retract from the rest position to the open position. Then the slide panel will extend to the lower position towards the back of the whole machine. In the next procedure, the sweep panel will extend to the closed position. The refuse in the hopper will be swept and packed. Finally, the slide panel will retract to the raised position and move the refuse into the body [48].

During the garbage collection cycle, the above two steps will be repeated for several times. The working time of these two steps is between 10 to 60 seconds from the daily working measurement. In the simulation, it will take 30 seconds [49, 50].

There are two different technologies for the garbage collection system. The most common one is the electro-hydraulic PTO. For this system, all the movements of the

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garbage collection will be powered by the hydraulic system. The hydraulic pump will be powered by an independent electric motor. The motor has its independent inverter [51]. For a single cycle, the mechanical energy demand of the hydraulic system is about 225 kJ [49, 50]. The efficiency of the electro-hydraulic system is about 90% and the efficiency of the inverter is around 99% [12, 52]. Thus, for the electro-hydraulic PTO, the electrical energy at the panel for a single collection is about 250 kJ. A single collection cycle is set to be 30 seconds. Therefore, the average electrical power for a single garbage collection is 8.333 kW.

Another PTO technology is the pure electric system. For this system, there is no hydraulic equipment or function. All the movements for garbage collection will be achieved by the electric motors with higher efficiency and lower noise. For the pure electric PTO, the electrical energy demand at the panel for a single collection is 166 kJ [53]. Covert to power with 30 seconds is 5.533 kW.

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Chapter 3 Vehicle Model

In this chapter, the basic parameters for different trucks, such as curb mass, maximum payload, tyre dimension, and vehicle structure, are introduced. The electric machine with its performance and the rotational inertia is presented along with the gear ratio of the gearbox and axle gear. All the values mentioned in this chapter will be directly used in the VECTO and MATLAB simulations.

3.1 Vehicle Parameters

As mentioned in Section 1.2, this master thesis contains four different truck types: 2- axle delivery truck, 3-axle garbage truck, 5-axle tractor trailer, and 8-axle combi. For the curb mass, the value is estimated by the best practice values from YDMC, Scania.

The legal max value of each truck is extracted from the regulations by YDRS¹, Scania.

In the simulation, each truck will simulate with three different load cases: unloaded, half payload, and legal max payload. The trucks masses for the simulation are shown in Table 3.1.1.

Table 3.1.1: Truck mass in simulation [T] (Source: YDMC)

Truck

Mass Empty Half Loaded Fully Loaded

Delivery 10.1 15.05 20

Garbage 16.7 22.35 28

Tractor 17.5 29.75 42

Combi 23.5 42.75 62

1YDRS: Regulations and Certifications, Road Safety

Analysis of the Energy Consumption of the Powertrain and the Auxiliary Systems for Battery-Electric Trucks

Analysis of the Energy

Consumption of the Powertrain and the Auxiliary Systems for Battery-Electric Trucks

GUANQIAO SONG

Author

Place for Project

Date for Presentation

Examiner

Supervisor

English Title

Svensk Titel

Abstract

Keywords

Sammanfattning

Nyckelord

Acknowledgements

Acronyms

Contents

1 Introduction

2 Background

3 Vehicle Model

4 Methodology

5 Results and Discussion

6 Conclusion

References

Chapter 1 Introduction

1.1 Company Presentation

1.2 Aim

1.3 Procedure

1.4 Limitation

1.5 Outline

Chapter 2 Background

2.1 Battery-Electric Truck Structure

2.2 Battery

2.2.1 State of Charge

2.2.2 Direct Current Internal Resistance of Battery Cell

2.2.3 Voltage of Battery Cell

2.2.4 Voltage, Resistance and Current of Battery Pack

2.2.5 Power and Energy of Battery Pack

2.3 Battery Direct-Current Charging

2.3.1 Charging Strategy

2.3.2 Charging Power and Losses

2.4 Powertrain Components

2.4.1 Panel

2.4.2 Power Electronic Converter (PEC) and Electric Machine

2.4.3 Mechanical Powertrain Components

2.5 Auxiliary System

2.5.1 Steering System

2.5.2 Pneumatic Compressor

2.5.3 Air Conditioning for Cooling

2.5.4 Cooling System Electrics

2.5.5 General Electric Systems

2.5.6 Auxiliary System Summary

2.6 Power Take Off (PTO)

Chapter 3

Vehicle Model

3.1 Vehicle Parameters