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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 5, NO. 4, DECEMBER 2019 1419
Function Development With an
Electric-Machine-in-the-Loop Setup: A Case Study
Konstantin Etzold , Timm Fahrbach, Serge Klein , René Scheer , Daniel Guse, Marc Klawitter,
Stefan Pischinger, and Jakob Andert
Abstract— In order to reduce automotive development times
and costs, particular development tasks are rescheduled to earlier
program phases (frontloading) by applying hardware-in-the-loop
(HiL) tests. However, there is a shortage of studies considering
HiL tests for function developments considering the thermal
behavior of electric drives. This article shall be a first step toward
closing this gap. A real-time co-simulation of a battery electric
vehicle and a driver model are developed and connected to an
electric traction machine at a laboratory test bench. A thermal
derating function is designed and calibrated at this test setup.
In particular, linear derating functions with different gradients
are implemented and tested for high load performances during
a track race, and the trade-off between energy demand and the
lap time is determined. Larger gradients of thermal derating
functions lead to shorter lap times and higher energy demands.
Thus, for this case study, an increase of the gradient of the
thermal derating function by a factor of two results in a lap time
improvement of 2.3% and a higher energy demand of 4.7%. The
test results demonstrate how HiL setups offer a favorable testing
scenario to calibrate thermal derating functions of electrified
powertrains in early development phases.
Index Terms—Electri c traction mot ors, frontloading,
hardware-in-the-loop (HiL), interior permanent magnet
synchronous machine (IPMSM), thermal derating.
I. INTRODUCTION
FOR automotive development programs of new electric
powertrains, investments in time and costs are required to
decrease steadily [3]–[8]. The development programs usually
follow the V-cycle with three particular phases. These are
system specification and implementation, followed by system
integration and thereafter by system test and validation (see
Fig. 1). Originally, these phases are conducted sequentially.
However, by rescheduling testing and validation tasks to earlier
program phases, several development steps can be parallelized.
This approach is usually called frontloading [3], [9]. Utilizing
frontloading, potential errors can be detected in earlier pro-
gram phases so that appropriate design changes can be applied
in a timely fashion. As a result, development time and costs
due to error fixing and design changes can be reduced [3]–[8].
Manuscript received June 21, 2019; revised September 20, 2019; accepted
October 23, 2019. Date of publication November 8, 2019; date of cur-
rent version January 7, 2020. This work was supported in part by the
European Union’s Horizon 2020 Research and Innovation Program under
Grant 769935 and in part by the Deutsche Forschungsgemeinschaft (DFG).
(Corresponding author: Konstantin Etzold.)
The authors are with the Institute for Combustion Engines, RWTH Aachen
University, 52062 Aachen, Germany (e-mail: etzold@vka.rwth-aachen.de).
Digital Object Identifier 10.1109/TTE.2019.2952288
Fig. 1. Frontloading approach by HiL component testing, displayed in the
V-cycle of automotive development programs, modified from [1], [2].
In this contribution, a hardware-in-the-loop (HiL) compo-
nent test of an electric traction machine (ETM) is presented.
This use case is an example of how functional development
tasks of prototype vehicles can be rescheduled to early devel-
opment phases according to the frontloading approach. For
HiL tests in general, the test object is connected with a
real-time simulation of the remaining system. By applying
this connection, the bidirectional interactions between the
test object and the remaining system are considered. Thus,
instead of studying the test object isolated from the remaining
system, the interdependencies of neighboring components and
the entire system can be investigated [10].
HiL setups of electric powertrains can be divided into signal,
electrical, and mechanical levels [11]–[14]. On the signal level,
the control unit is tested as a real component in connec-
tion with a real-time simulation of the electric powertrain
[15]–[17]. For the electrical level, the power electronics are
added to the control unit at the test bench, whereas the ETM
is simulated [18], [19]. On the mechanical level, the ETM
with power electronics and control units are tested as real
components, and the remaining powertrain and the vehicle are
simulated.
In this article, a mechanical HiL setup is utilized. Mechani-
cal HiL setups are an established testing environment for con-
ventional powertrains [10], [20]–[22], as well as for electrified
powertrains [14], [18], [23]–[31]. For instance, in [27], torque
vectoring functions of a battery electric vehicle (BEV) are
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
1420 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 5, NO. 4, DECEMBER 2019
Fig. 2. HiL setup with laboratory test bench and real-time simulation
platforms.
tested at a HiL setup focusing on the interactions between
electric drive and vehicle dynamic behavior. Another contri-
bution focuses on different energy storage systems of a parallel
hybrid powertrain [28]. In [30], an electric drive for a BEV
is tested on a HiL setup for different driving cycles, and
the test results are compared to simulations based on steady-
state measurements. It has been determined that the HiL mea-
surements and the simulation results differ, especially during
dynamic operations. This highlights the relevance of HiL tests.
However, there is a particular shortage of studies considering
mechanical HiL tests regarding the thermal behavior of electric
drives, especially thermal derating functions of electric drives
in interaction with the vehicle dynamics. This contribution
shall be a first step toward closing this gap.
In this contribution, thermal derating functions of a BEV
are developed and calibrated at a mechanical HiL setup on
a high load test track. In general, thermal derating functions
protect powertrain components from overheating by limiting
the available power of the electric drive in case the component
temperatures exceed certain thresholds [32]. The limiting
strategy of the electric drive has significant effects on vehicle
behavior and performance. A detailed evaluation of these
effects is the subject of this contribution. Thereto, the HiL
setup, including the laboratory test bench and the real-time
co-simulation environment, are described in Section II. In
Section III, the results are demonstrated with respect to the
effects of different thermal derating functions on torque oscil-
lations, vehicle velocity, and energy demand. Subsequently,
this article at hand is concluded in Section IV.
II. METHOD –HiLAPPROACH
The HiL setup is divided into two main sections. These
sections are the laboratory test bench and the real-time
co-simulations. Multiple physical quantities are exchanged
between the test bench components and the real-time sim-
ulation platforms. An overview of the relevant quantities is
provided in Fig. 2.
Fig. 3. Vehicle dynamics model, including a multi body system of wheels
and chassis within the environment frame.
TAB LE I
OVERVIEW OF RELEVANT VEHICLE DYNAMICS
SIMULATION PARAMETERS
A. Real-Time Co-Simulations
For the real-time simulations, a co-simulation approach
with two dedicated HiL simulators has been chosen. This
approach provides multiple processor cores and enables a
variable assignment of the simulation tasks to the individual
processor cores with respect to the simulation demand. Thus,
high computational power is required in order to meet real-
time conditions at a sample rate of 1 ms. Further advantages
of co-simulations are presented in [33].
The co-simulation setup consists of the HiL simulators A
and B shown at the bottom of Fig. 2. HiL simulator A is a
dSPACE Scalexio Processing Unit, and HiL simulator B is an
IPG Automotive xPACK4. Both HiL simulators are connected
via deterministic EtherCAT-Communication, complying with
real-time conditions. HiL simulator A performs the simulations
of the final drive and the power control unit (PCU) based on
the software MATLAB Simulink. HiL simulator B conducts
the simulations of the driver behavior and the vehicle dynamics
utilizing the software CarMaker [34], [35].
1) Vehicle Dynamics Simulation: For this use case,
an A-segment rear-wheel drive (RWD) BEV is investigated.
The most relevant vehicle parameters are listed in Table I,
and they are constant for all tests. The vehicle dynamics
model calculates the 3-D vehicle drive state for each time step
considering simulation parameters, as well as position- and
time-dependent inputs. Therefore, a multi body system based
on the guidelines introduced in [36] is computed, which has
been validated in [37] and [38]. The overall structure of the
vehicle dynamics model is displayed in Fig. 3.
Position-dependent inputs are provided by the road model.
This refers to, e.g., the 3-D alignment of the road expressed
in the environment frame Fre. Time-dependent inputs are
ETZOLD et al.: FUNCTION DEVELOPMENT WITH AN ELECTRIC-MACHINE-IN-THE-LOOP SETUP: CASE STUDY 1421
Fig. 4. Side view (left) of torque balance on a wheel and top view (right)
of acting forces on a wheel due to lateral dynamics.
transmitted to the vehicle dynamics model by the driver and
the final drive model, as shown in Fig. 2. The final drive
model provides the actual driveshaft torque Mact ,Whl of each
wheel, which is computed considering the measured torque
of the ETM at the test bench. The inputs from the driver
model are the steering wheel angle δStWhl , which determines
the vehicle yaw rate ψact ,zand the brake pedal force FBrkPdl.
It is used to calculate the mechanical brake force on each
wheel, considering the hydraulic ratio and the brake pressure
distribution between the front and rear axles.
The dynamics simulation divides the vehicle into five rigid
bodies interconnected by five joints. For each body, the motion
is described by differential and algebraic equations. The main
body is the chassis Frc. It is connected to each wheel carrier
body Frw,ij via suspension modules, and the environment Fre
as displayed in Fig. 3. Therefore, each wheel carrier moves
relative to the chassis as a function of the steering angle and
the suspension compression. The effective 3-D vectors for
the cutting moments and forces within each wheel carrier’s
coordinate systems are computed and then transformed into the
chassis’ coordinate system to calculate the driving resistances.
Due to their high relevance for the thermal behavior of
the powertrain components, the calculation of each wheel’s
speed nact,Whl and the vehicle velocity in longitudinal direction
vact,xwill be explained. The calculation of the wheel speed is
based on a torque balance for each wheel in its Frame Frw,ij,
depicted on the left in Fig. 4. The angular momentum of a
rotating mass equals the sum of all acting forces expressed in
its axis of rotation. Applied to a wheel, (1) yields the rotational
speed nWhl,ij, which depends on the inertia Iij, the actual
driveshaft torque Mact,Whl , the applied brake force FBand its
distance to the centerline rB, as well as on the tire contact
force in x-direction multiplied with the tire’s dynamic wheel
radius rdyn,ij
Iij ·2·π·˙nWhlij =Mact,Whlij −FBij ·rBij −Fxij ·rdynij .(1)
Fx,ij is an output of the utilized contact point interface (CPI)
tire model, which only describes the tire response forces and
torques in its contact point and neglects vertical deformations
[35]. Simplified, Fx,ij depends on the vertical wheel force
FZand the friction coefficient based on the slip of each tire
at the former time step. The vehicle velocity is calculated in
the chassis frame Frc, displayed in Fig. 3, by computing the
integration of the force balance of the vehicle. It considers
Fig. 5. Driver model with trajectory and velocity initialization.
the propelling force in the center of gravity Fprop, the rolling
resistance Fr,the driving resistance caused by lateral dynamics
Fx,l, as well as the air drag Fdand the grade resistance Fgas
displayed in the following equation:
meff ·dvact,x
dt
aact,x
=Fprop −Fr−Fx,l
Fxij
−Fd−Fg.(2)
Compared to longitudinal dynamics simulations similar to
[31] and [32], the applied 3-D vehicle dynamics simulation
also considers the lateral resistance force Fx,l. The lateral
resistance force is illustrated at the wheel’s top view in
Fig. 4. The centrifugal force Fyacts rectangular to the vehicle
velocity, which is compensated by the side force FS.The
side force acts rectangular to the wheel’s plane. However,
the wheel’s plane is tilted at an angle εto vact,x, which results
in the resistance force Fx,laccording to (3). The angle ε
depends on the sideslip angle of the vehicle αslip,aswellas
the steering angle δStWhl
Fx,lij =
i,j
FSij ·sin(εij(αslip,δ
StWhl)). (3)
The relevant outputs of the vehicle’s dynamics simulation
are the position of the vehicle with respect to the road,
the actual vehicle velocity, and the acceleration in the direction
of travel, as well as the actual yaw rate. These outputs are
transmitted to the driver model.
2) Driver Model: The driver model controls the vehicle’s
multidirectional behavior with respect to the requested driving
trajectory and the requested velocity, as well as the vehicle
drive state, which is transmitted from the dynamics simulation.
As illustrated in Fig. 5, the driver model is divided into the
driver simulation and the initialization, which is calculated
once at the beginning of a test.
Within the initialization, the requested driving trajectory
sreq, and the requested velocity profile vreq for the entire track
are determined. The determination of the requested trajectory
utilizes the given road coordinates xRand yRin order to
calculate a continuous spline as a function of xand yin the
environment coordinates, as well as a radius r(s)for each
curve. Thereafter, the radius r(s)is utilized to estimate the
requested vehicle velocity vreq(s)on the spline saccording to
vreq(s)=fax,max,ax,min,min ay,maxr(s), vmax .(4)
1422 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 5, NO. 4, DECEMBER 2019
TAB LE I I
OVERVIEW OF RELEVANT DRIVER SIMULATION PARAMETERS
The vehicle velocity is further adjusted by considering
the set maximum acceleration ax,max and deceleration ax,min
of Table II. In addition, smoothing functions are applied
to achieve a continuous velocity profile. For this article,
a medium aggressive velocity profile has been chosen, whose
parameters are presented in Table II [34].
The driver simulation is split into a lateral controller and
a longitudinal controller. The former utilizes the deviation of
the actual vehicle position to the requested trajectory in lateral
position yand the actual yaw rate ψact,zto set the necessary
steering wheel angle δStWhl. The longitudinal controller uses
the difference between the requested and actual velocity to
determine the force on the brake pedal FBrkPdl and the position
of the drive pedal sDrvPdl, which is transmitted to the PCU.
3) PCU: The PCU computes the torque request, which
is sent to the power electronics of the ETM utilizing the
drive pedal position and enforcing protection functions. The
translation of the drive pedal position sDrvPdl into a driver
torque request Mreq,Driver is conducted by a pedal map, which
is derived from a velocity-dependent pedal map of a series
production BEV of the A-segment class.
For component protection, the driver torque request
Mreq,Driver is subject to multiple software-based protection
functions. The thermal derating is the most relevant function
in this context since it affects the ETM operation significantly
by protecting it from overheating. The functionality of the
thermal derating is based on [32] and illustrated in Fig. 6.
The inputs of the thermal derating function are the ETM speed
nETM and the ETM temperature TETM, which is measured at
the winding head of the ETM at the test bench. Based on
lookup tables, the maximum torque of the ETM full load curve
Mfull,load and the thermal derating factor fDerating are set. The
thermal derating factor equals one, if the ETM temperature
TETM is smaller than a predefined lower temperature threshold.
For a TETM value larger than an upper temperature threshold,
the derating factor equals zero. In between these temperature
thresholds, the derating factor is linearly interpolated. For
the HiL tests presented in Section III, the lower temperature
thresholds are varied. Thus, the gradients of the thermal
derating function are varied as well, and the effects of the
gradient on the vehicle dynamics are investigated.
The maximum torque Mfullload and the thermal derating
factor fDerating are multiplied and yield to the maximum
torque Mmax,Motor for motor operation. Identically, the mini-
mum torque Mmin,Generator for generator operation is set. Both
quantities are the boundaries that limit the driver torque request
Mreq,Driver if necessary. The output of the thermal derating is
the final torque request Mreq,ETM , which is transmitted to the
power electronics at the laboratory test bench via deterministic
Gigalink-Communication.
Fig. 6. Implementation of thermal derating functions within the PCU.
In terms of thermal derating, the HV battery could be
another critical component. However, in [39], a HV battery for
a motorsport application has been presented. The critical time
constant of the thermal capacity from the HV battery is 2 h.
Compared to this time constant, the test duration of 12.5 min is
relatively short. Hence, regarding thermal derating, the battery
model is neglected in [39], as well as in the presented work.
B. Laboratory Test Bench Setup With ETM and Power
Electronics
The power electronics receive the torque request Mreq,ETM
from the PCU (see Fig. 2). According to this torque request,
the power electronics control the ac currents for the ETM
at an inverter operation frequency of 10 kHz. The inverter
control is based on the algorithms for maximum torque per
ampere (MTPA) and flux weakening and maximum torque per
voltage (MTPV). These control algorithms are implemented
by lookup tables with particular Idand Iqcurrent values for
each operation point. The investigated ETM is an interior
permanent magnet synchronous machine (IPMSM) with a
maximum torque of 160 Nm and a maximum mechanical
power of 82 kW. It is connected to the load machine via
a torque measurement flange, which sends the contactless
measured torque Mact,ETM to the final drive simulation of
HiL simulator A. From the final drive, the measured torque
Mact,ETM is converted into the wheel torques Mact,Whl,which
lead to an acceleration and change of velocity of the vehicle
model at simulator B. The simulated vehicle velocity is the
feedback signal for the driver model in order to control the
vehicle velocity by requesting a particular torque considering
the protection functions of the PCU. Thus, this HiL setup pro-
vides a comprehensive testing environment with a measured
torque feedback from the test bench. This feedback enables
investigations on the torque functions of the PCU in interaction
with the driver behavior and the ETM test bench.
For setting the ETM to the corresponding speed of the
vehicle velocity, the ETM speed nETM is transmitted from
ETZOLD et al.: FUNCTION DEVELOPMENT WITH AN ELECTRIC-MACHINE-IN-THE-LOOP SETUP: CASE STUDY 1423
Fig. 7. Continuous operation: simulated temperature curve for PT1 transfer
function with time delay and measurement at 5000 r/min and 80 Nm.
TABLE III
TRANSFER FUNCTION PARAMETERS OF THE ETM
the final drive simulation of HiL simulator A to the power
electronics of the load machine (see Fig. 2). Here, the ETM
speed is adjusted by a proportional-integral-derivative (PID)
controller. The control parameters are determined by manual
calibration in order to achieve the best possible trade-off
between stable and highly dynamic operation. The control
parameters are kept constant for all tests.
C. Initial Calibration of Thermal Derating Function
The temperature of the ETM can be described as a function
of the thermal capacity CETM, coolant temperature Tcool,
thermal resistance Wand the ETM losses Ploss with time delay
td
CETM ·dT
ETM(t)
dt =Tcool −TETM(t)
W+Ploss(t−td). (5)
Equation (5) is a first-order differential equation, which can
be described as a transfer function with time constant (PT1)
and time delay according to the Laplace transformation
G(s)=Kp
ts·s+1·e−s·td.(6)
The characteristic parameters are the proportional factor
Kp, the time constant ts, and the time delay td.These
parameters are approximated based on measurement data. For
a continuous operation point of 80 Nm and 5000 r/min, the
saturation of the ETM temperature is measured over 20 min
(see Fig. 7). For this operation point, the thermal ETM
behavior can be approximated by the transfer function (6) with
the torque as input and the temperature as output variables.
The characteristic parameters are presented in Table III.
However, applying the parameters of continuous operation,
the simulated ETM temperature is below the measurements
Fig. 8. Peak operation: simulated temperature curve for PT1 transfer function
with time delay and measurement.
for peak operation. This is illustrated in Fig. 8, in which
the heating curves for maximum torque at 4000, 10 000,
and 14 000 r/min are depicted. Hence, in a second iteration,
the parameters of the transfer function for continuous oper-
ation are adjusted. The parameters for peak operation are
presented in Table III. These parameters are a worst-case
approximation in order to meet the maximum measured ETM
temperatures.
The ETM model with (6) is combined with the derating
function of Fig. 6, and critical torque step responses are
evaluated. In Fig. 9, the interaction between the ETM model
and the derating function is illustrated. For a maximum driver
torque demand of 160 Nm, the ETM temperature increases
from 60 ◦C. At the lower derating threshold, the torque
is reduced due to thermal derating. For stable temperature
control, the lower derating threshold is calibrated in such a way
that the ETM temperature does not exceed the upper derating
threshold due to an overshoot of the ETM temperature. For
a lower derating threshold of 125 ◦C, the ETM temperature
increases up to 155 ◦C at 44 s and does not exceed the upper
temperature threshold. Thus, 125 ◦C is selected as an optimum
setting, and this derating strategy is called L-125-155.
III. RESULTS
In terms of stability, the thermal derating function
L-125-155 has been derived from the simulation in
Section II-C. In this section, the interaction between varying
thermal derating functions, the virtual vehicle, and the ETM
at the test bench are investigated in terms of lap time and
the dc energy demand. Similar to the implementation of mul-
tiple series production vehicles, linear derating strategies are
applied. E.g., for the derating strategy L-125-155, the derating
factor decreases linearly from one at the lower derating thresh-
old of 125 ◦C to zero at the upper derating threshold of 155 ◦C.
For all derating strategies, the upper derating threshold is the
same. Considering manufacturer standards, the upper derating
threshold of the ETM windings yield to 155 ◦C. The derating
1424 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 5, NO. 4, DECEMBER 2019
Fig. 9. Calibration of thermal derating function with driver torque request
of 160 Nm and no temperature overshoot beyond the upper derating threshold.
Fig. 10. Different linear thermal derating functions applied to electric drive.
strategies vary depending on the lower derating threshold,
which leads to different gradients of the derating functions
(see Fig. 10).
For studying the ETM’s thermal behavior, one lap of the
Nuerburgring Nordschleife has been chosen as a high load
test scenario. This test track has a length of 20.7 km, with
severely altering elevations (see Fig. 11).
For all test scenarios, the thermal settings of the test bench
conditioning system are the same. The volumetric cooling
flow rate of the ETM is set to 8 L/min and 60 ◦C. The
inverter is cooled at 11 L/min and 11 ◦C. Compared to the
ETM setting, the inverter is conditioned at a higher volumetric
flow rate and a significantly lower temperature, so that the
inverter temperature does not reach its lower thermal derating
threshold during the following HiL tests. Therefore, thermal
derating due to inverter overheating cannot occur, and the
HiL measurements are not affected by a possible thermal
derating of the inverter. By applying the same thermal starting
conditions, different linear derating strategies are tested at
the HiL setup for one lap on the test track. The results are
presented in Fig. 12, and the effects of the thermal derating
strategies on the lap time and dc energy demand are discussed
in Sections III-A and III-B.
Fig. 11. High load test track with slope and elevation profiles.
Fig. 12. HiL measurements of dc energy demand and lap time for different
thermal derating functions.
A. Effects of the Gradients of the Thermal Derating
Functions on DC Energy Demand and Lap Time
By comparing the thermal derating strategy L-130-155 to
L-105-155, the lap time is significantly reduced by 17 s.
Simultaneously, the dc energy demand increases by almost
1.4 kWh/100 km (see Fig. 12). Hence, an increase of the ther-
mal derating gradient by a factor of two leads to an increased
dc energy demand of 4.7% and a lap time improvement of
2.3%. This lap time improvement is due to a higher average
velocity. In Fig. 13, the velocity profiles for the derating strate-
gies L-130-155 and L-105-155 are illustrated. Both velocity
profiles show a good congruence at the beginning of the test
track, where the vehicle drives downhill, and the ETM is not
required to operate at its maximum power. Thus, the ETM
temperatures are significantly below the derating threshold,
and the different thermal derating functions do not affect the
ETM power and the corresponding vehicle velocity. However,
ETZOLD et al.: FUNCTION DEVELOPMENT WITH AN ELECTRIC-MACHINE-IN-THE-LOOP SETUP: CASE STUDY 1425
Fig. 13. Velocity profile and dc energy demand for the derating strategies
L-105-155 and L-130-155.
especially in the section from 8 to 11 km, the vehicle velocity
is significantly lower with the L-105-155 strategy than with
the L-130-155 alternative. This section is depicted in Fig. 13.
In the center are the profiles of the driver torque requests
Mreq,Driver, and the torque requests Mreq,ETM, which is limited
due to the thermal derating function of the PCU.
By starting at 8.1 km, the vehicle is driven uphill, and
the driver requests the maximum torque of 160 Nm. For
L-130-155, the ETM temperature exceeds the lower derating
threshold by almost 2 ◦C, and subsequently, the torque request
is derated to 150 Nm. For the derating strategy L-105-155,
the lower derating threshold is exceeded by almost 15 ◦C.
Hence, the driver torque request is reduced even more to
115 Nm. As a consequence, the vehicle acceleration is slower.
However, due to the higher torque of test L-130-155,
the ETM temperature increases up to 147 ◦C at a distance
of 8.7 km. Hence, the driver torque request is reduced to
42 Nm, and the vehicle acceleration reduces, which is deter-
mined by a lower velocity gradient in Fig. 14. In contrast,
the ETM temperature of L-105-155 increases less so that the
ETM torque is higher. For a short moment, this leads to a
higher velocity compared to L-130-155 at 8.6 km. However,
the majority of the velocity of L-105-155 is equal to or smaller
than L-130-155, which is the reason for the higher lap time.
A similar behavior was determined for the other derating
strategies L-115-155 and L-125-155, and in summary, it can
be concluded that within particular limits, steeper thermal
derating gradients lead to a decrease of lap time.
B. Oscillations Due to the Interactions Between Thermal
Derating Strategy and Thermal Behavior of the Electric
Drive
For derating strategies from L-105-155 to L-130-155,
steeper thermal derating gradients lead to a decrease of lap
Fig. 14. Derated torque in interaction with the vehicle velocity and the ETM
temperature for the thermal derating functions L-105-155 and L-130-155.
time. However, there is a particular limit to this correlation.
For derating strategies larger than L-130-155, it is determined
that the lap time increases instead of further decreasing (see
Fig. 12). The reason for this are the torque oscillations,
which are due to oscillating ETM temperatures in interaction
according to the derating functions.
These oscillations are illustrated in Fig. 15 for the thermal
derating strategy L-145-155. At the distance of 9.1 km,
the ETM temperature is 140 ◦C, which is below the lower
derating threshold of 145 ◦C. Hence, the driver torque request
Mreq,Driver of 160 Nm is met by the torque request Mreq,ETM
from the PCU, without a reduction by the thermal derating
function. In consequence of the high torque, the ETM tem-
perature increases, and the PCU reduces the torque request
at a distance of 9.2 km. However, due to the thermal inertia
of the ETM, the ETM temperature continues to rise up to
159 ◦C, which even exceeds the upper derating threshold
of 155 ◦C. As a result, the ETM torque is set to 0 Nm at a
distance of 9.4 km. The described interactions between thermal
inertia and the thermal derating function lead to the oscillations
of the ETM temperature and the ETM torque. Moreover,
comparing the magnitude of the torque oscillation of the
thermal derating strategy L-145-155 to L-130-155, it turns out
that these oscillations increase with a higher gradient of the
thermal derating function (see Fig. 15).
1426 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 5, NO. 4, DECEMBER 2019
Fig. 15. Derated torque in interaction with the vehicle velocity and the ETM
temperature for the thermal derating functions L-145-155 and L-130-155.
Fig. 16. Control loop with PT1 element for thermal behavior of ETM and
P-controller for derating function.
In theory, the increase of oscillations can be illustrated in
the root locus curve of Fig. 16. Since the root locus can only
be applied for rational transfer functions, the controlled system
equation (6) with a time delay element of the ETM is required
to be transformed into a rational function. Utilizing the pade
approximation, (6) can be written as [40]
G(s)=Kp
ts·s+1·1−0.5·tt·s
1+0.5·tt·s.(7)
The thermal behavior of the ETM is described by (7). There
is one pole at −0.01 related to the time constant ts. The second
pole is at −0.25, corresponding to 0.5 for the time delay tt.The
derating function can be described as a proportional controller
in interaction with the controlled ETM system (see Fig. 16).
Fig. 17. Visualization of the thermal ETM system G(s) in interaction with
the thermal derating function in a root locus curve.
TAB LE I V
PROPO RTIO NAL CONSTANTS FOR VARYING DERATING FUNCTIONS
In the root locus curve, the proportional controller and the
controlled system are considered (see Fig. 17). For different
derating factors Kc, the stability of the system and the damping
correlation are determined. For proportional controller settings
with small Kcvalues, the damping factor increases, which
explains lower oscillations in the controlled system. This is
illustrated in Table IV; the damping factor increases from
0.24 to 0.6 for the derating strategies L-130-155 to L-105-
155. In terms of stability, for derating strategy L-145-155, Kc
equals 16 Nm/◦C, and the pole pairs have a positive real part.
Hence, the entire system becomes unstable. The instability is
determined in the HiL measurements by the ETM temperatures
exceeding the upper temperature threshold of 155 ◦C(see
Fig. 15). Also, the instability can be determined by the torque
requests, which oscillates between the limitations of 0 and
160 Nm.
Regarding lap time and energy demand, the torque oscil-
lations set a particular limit to the gradient of the ther-
mal derating function. For the derating function L-145-155,
the dc energy demand and the lap time are significantly higher
than for the derating functions L-115-155 and L-125-155
(see Fig. 9). Moreover, for the thermal derating func-
tion L-135-155, the dc energy demand increases by almost
0.1 kWh/100 km, and the lap time increases by one second
compared to L-130-155. Hence, regarding lap time and energy
demand, the steepest derating gradient is L-130-155.
As demonstrated, for linear derating functions, there is a
particular trade-off between lap time improvement by increas-
ing thermal derating gradients and higher magnitudes of torque
and ETM temperature oscillations. For future work, non-linear
and more advanced derating functions shall be investigated,
e.g., thermal derating functions with damping strategies similar
ETZOLD et al.: FUNCTION DEVELOPMENT WITH AN ELECTRIC-MACHINE-IN-THE-LOOP SETUP: CASE STUDY 1427
TAB LE V
CHARACTERISTICS OF THE TEST BENCH SETUP
TAB LE V I
ABBREVIATION TABLE
to [32] or ETM model-based strategies applying a model-
predictive strategy (MPC) for the ETM temperature control
[41]. However, this use case clearly demonstrated how the
interactions between the thermal behavior of the ETM and
the vehicle performance could be studied with the presented
HiL setup. This is another example of how HiL setups
can be favorably applied to develop, calibrate, and validate
thermal powertrain control functions by means of laboratory
test benches. In the presented case study, test benches can
deliver a decisive contribution for efficient frontloading for
automotive development programs because complex technical
dependencies do not have to be simulated in detail (here:
thermal behavior of the ETM), they can be tested directly due
to the availability of real hardware.
IV. CONCLUSION
In this article, the feasibility of a HiL setup for thermal
calibration tasks of electric powertrains is shown. Thereto, a
real-time cosimulation of a BEV in interaction with a driver
model is developed and combined with an ETM installed on
a laboratory test bench. Different gradients of linear thermal
derating functions are implemented and tested considering
vehicle operations at a high load test track. It is determined
that steeper gradients lead to shorter lap times and higher
energy demands. In particular, an increased gradient of the
thermal derating function by a factor of two leads to a lap
time improvement of 2.3% combined with a 4.7% higher
energy demand. However, a higher thermal derating gradi-
ent in interaction with the thermal behavior of the ETM
causes increased magnitudes of torque oscillations. Therefore,
the increase of the thermal derating gradient is limited. These
test results are based on a HiL test setup considering the
TAB LE V II
FORMULA SYMBOL TABLE
interdependencies between thermal behavior of the ETM at the
test bench, the virtual vehicle performance, as well as thermal
derating strategies of the PCU. This highlights the relevance of
HiL testing for thermal calibration tasks in early development
phases, and it represents an additional example of how HiL
testing can enhance frontloading for automotive development
programs.
1428 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 5, NO. 4, DECEMBER 2019
ACKNOWLEDGMENT
The tests were partially conducted at the Center for Mobile
Propulsion (CMP), RWTH Aachen University.
The authors would like to thank DENSO Automotive
Deutschland GmbH, IPG Automotive GmbH and dSPACE
GmbH for the supply of hardware and software, as well as
the post graduate program GRK1856 for the interdisciplinary
scientific knowledge exchange.
APPENDIX
See Tables V–VII.
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ETZOLD et al.: FUNCTION DEVELOPMENT WITH AN ELECTRIC-MACHINE-IN-THE-LOOP SETUP: CASE STUDY 1429
Konstantin Etzold received the bachelor’s and
master’s degrees from RWTH Aachen University,
Aachen, Germany, in 2015 and 2016, respectively,
where he is currently pursuing the Ph.D. degree with
the Junior Professorship for Mechatronic Systems
for Combustion Engines.
Timm Fahrbach received the bachelor’s and
master’s degrees from RWTH Aachen University,
Aachen, Germany, in 2017 and 2019, respectively,
where he is currently pursuing the Ph.D. degree with
the Junior Professorship for Mechatronic Systems
for Combustion Engines.
Serge Klein received the bachelor’s and master’s
degrees from Duisburg-Essen University, Duisburg,
Germany, in 2011 and 2013, respectively. He is
currently pursuing the Ph.D. degree with the Junior
Professorship for Mechatronic Systems for Combus-
tion Engines, RWTH Aachen University, Aachen,
Germany.
René Scheer received the bachelor’s and master’s
degrees from RWTH Aachen University, Aachen,
Germany, in 2015 and 2017, respectively, where
he is currently pursuing the Ph.D. degree with the
Junior Professorship for Mechatronic Systems for
Combustion Engines.
Daniel Guse received the bachelor’s and master’s
degrees from RWTH Aachen University, Aachen,
Germany, in 2013 and 2015, respectively, where
he is currently pursuing the Ph.D. degree with the
Institute for Combustion Engines.
Marc Klawitter received the bachelor’s degree
from RWTH Aachen University, Aachen, Germany,
in 2018, where he is currently pursuing the master’s
degree.
Stefan Pischinger received the Diploma in mechan-
ical engineering from RWTH Aachen University,
Aachen, Germany, in 1985, and the Ph.D. degree
from the Sloan Automotive Laboratory, Massa-
chusetts Institute of Technology, Cambridge, MA,
USA, in 1989, with a focus on spark ignition in
modern combustion engines.
Since 1997, he has been a Professor with RWTH
Aachen University, where he also has been the
Director of the Institute for Combustion Engines.
Jakob Andert received the Ph.D. degree from
RWTH Aachen University, Aachen, Germany, in
2012, with a focus on real-time optimization for
controlled auto ignition (CAI).
Since 2014, he has been directing the Junior Pro-
fessorship for Mechatronic Systems for Combustion
Engines, RWTH Aachen University.
