Energy and thermal management, air-conditioning, and waste heat utilization

Energy and Thermal

Management,

Air-Conditioning,

and Waste

Heat Utilization

Christine Junior

Oliver Dingel Editors

2nd ETA Conference, November 22–23,

2018, Berlin, Germany

Energy and Thermal Management, Air-Conditioning,

and Waste Heat Utilization

Christine Junior • Oliver Dingel

Editors

Energy and Thermal

Management,

Air-Conditioning, and Waste

Heat Utilization

2nd ETA Conference, November 22–23,

2018, Berlin, Germany

123

Editors

Christine Junior

Engineer Society Automobile and

Traffic IAV GmbH

Gifhorn, Germany

Oliver Dingel

Engineer Society Automobile and

Traffic IAV GmbH

Chemnitz, Germany

ISBN 978-3-030-00818-5 ISBN 978-3-030-00819-2 (eBook)

https://doi.org/10.1007/978-3-030-00819-2

Library of Congress Control Number: 2018960428

© Springer Nature Switzerland AG 2019

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Foreword

The efficient and intelligent use of energy resources is of key importance to our

future in transport, industrial, and building services. As a result, the sparing use and

the exploitation of as-yet-unused energy resources are attaining ever greater

importance. In order to draw on existing potential and also generate new ideas, all

the relevant energy and heat flows will need to be considered. This means that

energy and thermal management, air-conditioning, and waste heat utilization are

today analyzed across the board in the search for solutions.

However, the development of cross-sectoral solutions and ideas is not affected

only by physics but also lasting influenced by underlying frameworks. Due to the

demands of society and policymakers, the requirements concerning the efficient

utilization of energy are subject to constant change. In addition, the wealth of

technically feasible solutions is generating increasing complexity within the

development process. Thus, interdisciplinary and cross-sectoral solutions are chal￾lenged by new constraints which are impacting future concepts and components.

But how do sustainable solutions and innovations in energy and thermal man￾agement, air-conditioning, and waste heat utilization need to be structured for this

changing environment of the future?

ETA 2018 offers answers to this question and shares the latest research results,

innovative technologies, and best practices. Be inspired by approaches, technical

solutions, and possibilities for an energy-efficient future!

November 2018 Christine Junior

Oliver Dingel

v

Contents

Energy and Thermal Management

Choice of Energetically Optimal Operating Points in Thermal

Management of Electric Drivetrain Components .................. 3

Carsten Wulff, Patrick Manns, David Hemkemeyer, Daniel Perak,

Klaus Wolff, and Stefan Pischinger

Higher Cruising Range Through Smart Thermal Management

in Electric Vehicles – Interaction Between Air Conditioning

and Cooling System Components in the Overall Network ........... 15

Daniel Moller, Jörg Aurich, and Ronny Mehnert

Auxiliary Heating, Cooling and Power Generation in Vehicles

Based on Stirling Engine Technology ........................... 30

Hans-Detlev Kühl

Experimental Investigation on Effect of Fuel Property on Emissions

and Performance of a Light-Duty Diesel Engine .................. 40

M. Thamaraikannan, P. L. Rupesh, K. Raja, and K. Manideep

Conception and First Functional Tests of a Novel Piston-Type Steam

Expansion Engine for the Use in Stationary WHR Systems .......... 49

Michael Lang, Christian Bechter, Sebastian Schurl,

and Roland Kirchberger

Thermal High Performance Storages for Use in Vehicle Applications... 66

Werner Kraft, Veronika Jilg, Mirko Klein Altstedde, Tim Lanz,

Peter Vetter, and Daniel Schwarz

Determination of the Cooling Medium Composition in an Indirect

Cooling System ............................................ 80

Alexander Herzog, Carolina Pelka, Rudolf Weiss, and Frank Skorupa

vii

Air Conditioning

Approach for the Transient Thermal Modeling of a Vehicle Cabin .... 101

David Klemm, Wolfgang Rößner, Nils Widdecke,

and Jochen Wiedemann

Personalized Air-Conditioning in Electric Vehicles Using Sensor

Fusion and Model Predictive Control ........................... 119

Henning Metzmacher, Daniel Wölki, Carolin Schmidt,

and Christoph van Treeck

Simply Cozy - Adaptive Controlling for an Individualized

Climate Comfort .......................................... 130

Martin Noltemeyer, Lanbin Qiu, Christine Susanne Junior,

Thomas Wysocki, Johannes Ritter, and Jan Ackermann

Waste Heat Recovery

Waste Heat Recovery Potential on Heavy Duty Long Haul

Trucks – A Comparison ..................................... 141

Thomas Reiche, Francesco Galuppo, and Nicolas Espinosa

Combining Low- and High-Temperature Heat Sources in a Heavy

Duty Diesel Engine for Maximum Waste Heat Recovery

Using Rankine and Flash Cycles .............................. 154

Jelmer Rijpkema, Karin Munch, and Sven B. Andersson

Simulative Investigation of the Influence of a Rankine Cycle Based

Waste Heat Utilization System on Fuel Consumption

and Emissions for Heavy Duty Utility Vehicles ................... 172

Kangyi Yang, Michael Grill, and Michael Bargende

Requirements for Battery Enclosures - Design Considerations

and Practical Examples ..................................... 194

Jobst H. Kerspe and Michael Fischer

Design of a Thermoelectric Generator for Heavy-Duty Vehicles:

Approach Based on WHVC and Real Driving Vehicle

Boundary Conditions ....................................... 206

Lars Heber, Julian Schwab, and Horst E. Friedrich

Author Index................................................ 223

viii Contents

Energy and Thermal Management

Choice of Energetically Optimal Operating

Points in Thermal Management of Electric

Drivetrain Components

Carsten Wulff1(&)

, Patrick Manns2

, David Hemkemeyer2

,

Daniel Perak2

, Klaus Wolff2

, and Stefan Pischinger1

1 RWTH Aachen University, Institute for Combustion Engines,

Forckenbeckstr. 4, 52074 Aachen, Germany

[email protected] 2 FEV Europe GmbH, Neuenhofstr. 181, 52078 Aachen, Germany

Abstract. Increasing the efficiency of electric vehicles is a development focus

in the automotive industry in order to reach the range targets set by customer

requirements. Thermal management can have a positive effect on the system

efficiency of electric vehicles. In this contribution, a simulation model of the

drivetrain and cooling system of an electric vehicle has been build up. The aim

is to investigate the influence of the cooling system control and resulting

component temperatures on the drivetrain efficiency. Thus, energetically optimal

target temperatures for inverter and motor can be identified and implemented in

the cooling system control.

This approach goes beyond the state of the art control strategy of keeping the

temperatures under the component protection threshold. Related research sug￾gests that the component efficiency of inverter and motor can be increased by

reducing their operation temperature. The simulation results in this article show

that choosing target temperatures for inverter and motor below the components’

safety limit can have a small, positive impact on the system efficiency of the

electric vehicle.

As the model is yet to be validated, these results implicate that the optimal

component target temperatures for inverter and motor regarding system effi￾ciency are below the protective limit. As a next step, the model will be validated

with comprehensive component and vehicle measurement data in order to give a

quantitative statement on the possible benefits of optimized thermal manage￾ment control.

Keywords: Electric vehicles
Thermal management
Optimal control

1 Introduction

Vehicle range shows to be a major contributor to the consumer acceptance of battery

electric vehicles. As the battery capacity installed into a vehicle is limited by cost- as

well as weight-considerations, one development focus for electric vehicles lies in the

improvement of the system efficiency. [1] Thermal management is seen as a consid￾erable factor in the system efficiency of battery electric vehicles [2].

© Springer Nature Switzerland AG 2019

C. Junior and O. Dingel (Eds.): ETA 2018, Energy and Thermal Management,

Air-Conditioning, and Waste Heat Utilization, pp. 3–14, 2019.

https://doi.org/10.1007/978-3-030-00819-2_1

This paper aims to investigate the effects of the cooling of electric drivetrain

components on the system efficiency of a battery electric vehicle. To this end, a

simulation model is developed which simulates the energy flows within the electric

drivetrain of an A-Segment BEV.

The model includes map-based models for an inverter as well as motor and

transmission, which simulate the effects of component temperatures onto their effi￾ciency. The simulation model features comprehensive models for the cooling system as

well as the vehicle longitudinal dynamics in order to simulate the system energy

consumption. This model is used to determine the energy consumption of the drivetrain

as well as the cooling circuit components under various ambient and operating con￾ditions. Finally, an analysis of these results is conducted to find energetically optimal

operating points and control strategies for the cooling system of battery electric

vehicles.

2 Simulation Model

The simulation model is composed of three main parts:

1. The drivetrain model, which consists of a simplified longitudinal dynamics model

for the calculation of the loads for the drivetrain, and map-based models for the

transmission, electric motor and inverter.

2. The cooling circuit model, which consists of physical models for the coolant tubes

as well as degas-bottle and map-based models for the coolant pump and radiator.

3. The map-based underhood-model, which thermally links the other submodels by

calculating the relative air speeds and ambient temperatures for all other

components.

The model has been implemented in Matlab Simulink. The following sections

provide a detailed description of these submodels.

2.1 Drivetrain Model

The drivetrain is modeled as an inverse model in which the desired vehicle speed from

the drive pattern acts as an input to a signal path. Along this path the required power

demand in order to follow the drive pattern is calculated (see Fig. 1).

Within the drivetrain model, the model control provides the desired speed and

gradient to the vehicle model. In the vehicle model, the drive resistance resulting from

the given drive pattern is being calculated with a simple longitudinal dynamics mode

[3, 4]. The resulting wheel torque and speed are propagated to the transmission model.

The transmission model calculates the resulting motor speed with the final drive ratio

and uses an efficiency map to calculate the required motor torque. This efficiency map

uses wheel torque and transmission oil temperature as inputs. The consecutive motor

and inverter model also use efficiency maps to calculate the resulting power demand

for the given drive pattern. These efficiency maps use the component temperatures as

an additional dimension.

4 C. Wulff et al.

2.2 Cooling Circuit and Underhood Model

The cooling circuit model consists of physical models for the coolant tubes as well as

the degas bottle. The models for the radiator and the coolant pump are map-based (see

Fig. 2). For a given pump speed the pump model calculates the volume flow in the

cooling circuit based in the resulting pressure drop of the cooling circuit. Motor as well

as inverter are part of the cooling circuit model, with physical hydraulic models for the

calculation of the pressure drop [5].

Fig. 1. Signal flow in the inverse model of the drivetrain

Fig. 2. Integration of drivetrain and cooling circuit into underhood model

Choice of Energetically Optimal Operating Points in Thermal Management 5

The underhood model consists of a single air volume, which represents the thermal

mass of the engine compartment air within the vehicle. The inlet air flow is calculated

with a map depending on vehicle and fan speed. This airflow is zero when the radiator

shutter is closed. The radiator shutter also changes the drag resistance coefficient within

the vehicle model depending on its state.

2.3 Energy Flows Within the Model

As this model is designed to simulate the influence of the cooling system on the electric

drivetrain components, in addition to the electric and mechanical energy flows all

relevant thermal energy flows are modeled. This enables a more precise prediction of

the temperatures of the electric drivetrain components.

The thermal energy flows that have been included comprise all heat transfer

mechanisms. The radiation losses towards the engine compartment are modeled

physically based on the components’ temperature, surface area and emissivity. Con￾ductive heat transfer is considered between the transmission and motor, as those are

physically joined in the reference vehicle. Conductive heat transfer is also considered

within the thermal networks that model each components’ thermal behavior. The

amount of conductive heat transfer is determined by the temperature difference between

the thermal masses and fixed thermal resistances.

Three thermal masses are considered for the motor, the component housing, coolant

within the component and abstract inner thermal mass to simulate the relevant tem￾peratures for the component efficiency maps. For the motor, the temperature of the

inner mass represents the stator temperature. For the inverter, the thermal masses of the

housing is combined with the inner thermal masses. The resulting temperature of the

inverter’s thermal mass aims to simulate the temperature of the power electronics.

A separate thermal mass for the coolant is also part of the inverter model. For the

gearbox, only two thermal masses are considered. These are the combination of the

gears and housing and the oil.

The convective heat transfers considered are those between the components and the

engine compartment air as well as the heat transfer to the coolant circulating between

the drivetrain components. For these physical models, the heat transfer classes as

described in [6] are used. Also, the heat losses from the coolant tube surfaces to the

engine compartment are considered. For the heat transfer via the radiator, a map-based

approach is used, while the pump and degas-bottle are considered as adiabatic.

2.4 Model Parametrization

As the main aim of this article is to investigate the effects of the cooling circuit on the

system efficiency, the parametrization of the drivetrain components’ efficiency maps is

crucial. The efficiency maps for the components within this model are not only

dependent on speed and torque, but also on the component temperature. This enables a

simulation of the temperature-dependent behavior of the drivetrain. As the efficiency

maps that are provided by the manufacturers do not reflect the component temperature,

several assumptions have to be made in order to model the behavior of the drivetrain

6 C. Wulff et al.

components. The process of generating these temperature-dependent efficiency maps

shall be explained in the following chapters.

Inverter Efficiency Map. The inverter efficiency map provided by the manufacturer

has been measured at a constant coolant temperature. Information concerning the actual

temperature of the different inverter components at the time of measurement is not

available. Therefore, the temperature of the IGBTs and diodes have to be estimated in

order to separate the influences of the load and the device temperature within the

efficiency map. It is assumed, that the efficiency map has been measured in stationary

conditions and that all power losses within the inverter are dissipated by the coolant.

Furthermore, it is assumed that the temperature of the IGBTs and diodes TInverter is

equal to the derating temperature of the device TDerate when it is operated at maximum

continuous load. In peak load conditions, the junction and diode temperatures are

assumed as being equal to the derating temperature. For operating points below the

maximum continuous inverter load, the junction and diode temperatures are assumed to

be proportional to the inverter power loss in that point. At no load, these temperatures

are assumed to be equal to the coolant temperature TCoolant. The inverter temperatures

are calculated using (1)

TInverter¼TCoolant þ ð Þ TDerateTCoolant

Pact:

gMot;act

ð 1

gInv;act

1Þ

PMax;cont:

gMot;cont

ð 1

gInv;cont

1Þ ð1Þ

with PAct as the actual motor outlet power of a given point within the efficiency map of

the inverter, PMax,cont as the maximum continuous motor outlet power in the efficiency

map of the inverter and ηMot,act, ηInv,act, ηMot,cont, and ηInv,cont, as the respective effi￾ciencies in these points.

For the determination of the temperature-dependant losses of the inverter, the

approach developed by Feix et al. [7] is used. According to this approach, the switching

losses as well as the conduction losses can be calculated by using correlations. For the

conduction losses, Feix et al. [7] provide Eq. (2)

Ponð Þ¼ T fV1

100 C

T þ

5fV

4

VT0;25 C þ fR1

100 C

T þ

5fR

4

Ron;25 CIon
Ion ð2Þ

with T as the inverter temperature, fV and fR as material-specific factors and Ron;25 C

and VT0;25 C as device-specific parameters. The conduction losses Pon then result

depending on temperature and the current Ion, which is assumed to be equal to the DC￾current of the inverter. The DC-current can be calculated from the Inverter input power

and the Voltage of the DC source.

For the switching losses Feix et al. [7] provide another Eq. (3)

ESWð Þ¼ T fT1

100 C

T þ

5fT

4

1

fT

E125 C ð3Þ

with fT and E125 C as device specific parameters and ESW as the resulting switching

energy. Multiplied with the switching frequency, this results in the switching losses.

Choice of Energetically Optimal Operating Points in Thermal Management 7

For the determination of the temperature-dependent maps, the device-specific

parameters need to be known. To this end, a regression analysis is done with the known

temperatures, currents and losses from the given efficiency map for the prior calculated

temperatures in the given map (4)

PLoss¼aESWð Þ T fSW þ bPonðÞ ð T 4Þ

where PLoss is the power loss in a given point of the efficiency map, a and b as

weighing factors and the constant switching frequency fSW. As a result of the regression

analysis, a set of parameters is created which can be used for the generation of the

temperature-dependent efficiency map for the inverter.

Motor Efficiency Map. For the motor efficiency map, assumptions have to be made as

well due to limited information at hand. It is assumed, that the temperature-dependency

of the motor losses are mainly linked to the copper losses. Therefore, the temperature￾dependency of friction losses and iron losses within the motor is neglected [8]. The

copper losses of the motor are assumed to be solely linked to the known phase

resistance of the motor (5)

PLoss;copper ¼ R Tð ÞðI

2

q þ I

2

d Þ ð5Þ

where the copper losses of the Motor PLoss;copper are a result of the linearly temperature￾dependent phase resistance R Tð Þ and the two components of the phase current Iq and Id

[8].

In order to calculate a temperature-dependent efficiency map, the losses in the

known motor efficiency map need to be linked to respective temperatures. The

approach applied here is analog to the one applied to the inverter. With the known

temperatures and currents for the efficiency map of the motor, the copper losses can be

calculated within the given efficiency map. When deduced from the total losses, a map

of constant residue losses remains, which is not assumed to be temperature-dependent.

The full efficiency map for the motor is then calculated by adding the temperature￾dependent copper losses according to (5) to the map of residue losses for different

temperatures, thus adding the third dimension to the efficiency map.

Further Parametrization. The further parametrization of the model is being done by

using maps and parameters as provided by the manufacturers of the components within

the reference vehicle. For a comprehensive overview of the vehicle specifications,

please refer to the Annex.

3 Simulation Approach

As this contribution aims to evaluate the influence of the cooling system on the

drivetrain efficiency of an electric vehicle, the choice of the control strategy for the

cooling system is crucial for this investigation. Also, the choice of boundary conditions

for the simulation strongly influences the results. The control strategy as well as the

choice of boundary conditions are subject of the following chapters.

8 C. Wulff et al.

3.1 Cooling System Control Strategy

The control strategy for the cooling system applied within the model aims to control the

temperatures of the inner thermal mass of the electric motor and the inverter. The

temperatures of the inner thermal mass determine the efficiency of the component

together with the load point in the efficiency map. Therefore, the aim of the cooling

circuit controller is to keep the component temperatures below a desired target tem￾perature at minimum power consumption.

The actuators, which need to be controlled, are the coolant pump, the vehicle fan

and the radiator shutter. While all component temperatures are well below the desired

target temperature, the radiator shutter is closed and coolant pump and fan switched off.

When the component temperature reaches within 5 °C of the set target, the radiator

shutter is opened, enabling an airflow through the engine compartment. This affects the

drag resistance in the vehicle model. When the component temperature reaches the

desired target temperature, the coolant pump is switched on. The pump speed is

controlled by a PI-controller depending on the deviation of the component temperature

from the set target. If the deviation increases even if the pump has reached full speed,

the vehicle fan is engaged and also controlled with a PI-controller depending on the

component temperature. This control strategy is engaged when either of the compo￾nents reaches its target temperature, with the maximum of both temperature deviations

being the input for the controllers.

3.2 Boundary Conditions

The boundary conditions for the simulations carried out in this investigation refer to the

choice of driving cycle, ambient temperatures, start temperatures of the components as

well as the target temperatures set for the control of the cooling system. The WLTP

Class 3 is an industry standard in the evaluation of the power consumption of both

conventional and electric vehicles [3]. This representative driving cycle is chosen for

the evaluation of the drivetrain power consumption, as effects are evaluated on a system

level. Ambient temperatures of 20 °C and 40 °C are chosen to be evaluated in order to

compare normal and higher load conditions of the cooling system. Also, the starting

temperatures of the components are varied in order to evaluate the effect of the com￾ponents’ thermal mass on the load for the cooling system. Finally, the target temper￾atures for the inverter are varied in a range between 60 °C and 130 °C and for the

Table 1. Boundary conditions for the simulations

Drive

cycle

Ambient

temperature/°C

Component start

temperature/°C

Inverter target

temperature/°C

Motor target

temperature/°C

WLTP

Class 3

20 20 60, 70, …, 130 60, 70, …, 140

40 60, 70, …, 130 60, 70, …, 140

60 60, 70, …, 130 60, 70, …, 140

40 40 60, 70, …, 130 60, 70, …, 140

60 60, 70, …, 130 60, 70, …, 140

Choice of Energetically Optimal Operating Points in Thermal Management 9