ArticlePDF Available

Function Development With an Electric-Machine-in-the-Loop Setup: A Case Study


Abstract and Figures

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 tests. However, there is a shortage of studies considering Hardware-in-the-Loop 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 race track, and the trade-off between energy demand and 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 Hardware-in-the-Loop setups offer a favorable testing scenario to calibrate thermal derating functions of electrified powertrains in early development phases.
Content may be subject to copyright.
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.
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:
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
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
Fig. 2. HiL setup with laboratory test bench and real-time simulation
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.
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.
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
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
=Fprop FrFx,l
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 =
FSij ·sinijslip
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)
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
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
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
Fig. 7. Continuous operation: simulated temperature curve for PT1 transfer
function with time delay and measurement at 5000 r/min and 80 Nm.
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
dt =Tcool TETM(t)
W+Ploss(ttd). (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
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
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.
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
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,
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
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).
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]
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.
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
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.
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
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
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.
See Tables V–VII.
[1] M. Muli and J. Cassar, “Virtual validation—A new paradigm in controls
engineering,” in Proc. SAE Commercial Vehicle Eng. Congr., 2013,
pp. 1–7.
[2] J. Schäuffele and T. Zurawka, Automotive Software Engineering:
Grundlagen, Prozesse, Methoden und Werkzeuge, 2nd ed. Wiesbaden,
Germany: Vieweg+Teubner Verlag, 2004.
[3] E. Kerga, R. Schmid, E. Rebentisch, and S. Terzi, “Modeling the benefits
of frontloading and knowledge reuse in lean product development,” in
Proc. Portland Int. Conf. Manage. Eng. Technol. (PICMET), Honolulu,
HI, USA, Sep. 2016, pp. 2532–2542.
[4] J. Andert, S. Klein, R. Savelsberg, S. Pischinger, and K. Hameyer,
“Virtual shaft: Synchronized motion control for real time testing of
automotive powertrains,Control Eng. Pract., vol. 56, pp. 101–110,
Nov. 2016.
[5] F. Xia et al., “Crank-angle resolved real-time engine modelling:
A seamless transfer from concept design to HiL testing,” SAE Tech.
Paper 2018-01-1245, Apr. 2018.
[6] P. Schiffbaenker and R. Shankavaram, “Following the path to electri-
fication with a holistic battery development approach,” in Proc. IEEE
Transp. Electrific. Conf. (ITEC-India), Pune, India, Dec. 2017, pp. 1–6.
[7] M. Eisenbarth et al., “A holistic methodology for the development
of connected hybrid vehicles,” in Proc. Symp. Entwicklungsmethodik,
Wiesbaden, Germany, 2017, pp. 14–15.
[8] K. Etzold et al., “Virtuelle elektrifizierung–ein nahtloser entwick-
lungsprozess für closed-loop-testing von elektrischen antriebssträn-
gen,” in Proc. E-MOTIVE Expertenforum, Hannover, Germany, 2017,
pp. 109–112.
[9] S. Thomke and T. Fujimoto, “The effect of ‘front-loading’ problem-
solving on product development performance,” J. Product Innov. Man-
age., vol. 17, no. 2, pp. 128–142, 2000.
[10] H. K. Fathy, Z. S. Filipi, J. Hagena, and J. L. Stein, “Review of
hardware-in-the-loop simulation and its prospects in the automotive
area,” Proc. SPIE, vol. 6228, May 2006, Art. no. 62280E.
[11] A. Dhaliwal and J. Allen, “Hardware-in-loop simulation of electric
drives-description of a typical simulation platform,” SAE Tech. Paper
2009-01-2839, 2009.
[12] A. Wagener, T. Schulte, P. Waeltermann, and H. Schuette, “Hardware-
in-the-loop test systems for electric motors in advanced powertrain
applications,” SAE Tech. Paper 2007-01-0498, Apr. 2007.
[13] A. Bouscayrol, “Different types of hardware-in-the-loop simulation for
electric drives,” in Proc. IEEE Int. Symp. Ind. Electron., Cambridge,
U.K., Jun./Jul. 2008, pp. 2146–2151.
[14] F. Mocera and A. Somà, “Study of a Hardware-In-the-Loop bench
for hybrid electric working vehicles simulation,” in Proc. 12th Int.
Conf. Ecol. Vehicles Renew. Energies (EVER), Monte Carlo, Monaco,
Apr. 2017, pp. 1–8.
[15] A. Dhaliwal, S. C. Nagaraj, and S. Ali, “Hardware-in-the-loop simu-
lation for hybrid electric vehicles—An overview, lessons learned and
solutions implemented,” SAE Tech. Paper 2009-01-0735, 2009.
[16] T. Schulze, T. Schulte, and J. Sauer, “Hybrid drivetrain simulation
for hardware-in-the-loop applications,” SAE Tech. Paper 2011-01-0455,
[17] W. Li, L.-A. Gregoire, S. Souvanlasy, and J. Belanger, “An FPGA-
based real-time simulator for HIL testing of modular multilevel converter
controller,” in Proc. IEEE Energy Convers. Congr. Expo. (ECCE),
Pittsburgh, PA, USA, Sep. 2014, pp. 2088–2094.
[18] M. Lemaire, P. Sicard, and J. Belanger, “Prototyping and testing power
electronics systems using controller hardware-in-the-loop (HIL) and
power hardware-in-the-loop (PHIL) simulations,” in Proc. IEEE Vehicle
Power Propuls. Conf. (VPPC), Montreal, QC, Canada, Oct. 2015,
pp. 1–6.
[19] A. M. Zyuzev, M. V. Mudrov, and K. E. Nesterov, “PHIL-system for
electric drives application,” in Proc. 9th Int. Conf. Power Drives Syst.
(ICPDS), Perm, Russia, Oct. 2016, pp. 1–4.
[20] D. Guse et al., “Virtual transmission evaluation using an engine-in-the-
loop test facility,” SAE Tech. Paper 2018-01-1361, Apr. 2018.
[21] S. Klein, P. Griefnow, D. Guse, F. Xia, and J. Andert, “Virtual 48 V mild
hybridization: Efficient validation by engine-in-the-loop,” SAE Tech.
Paper 2018-01-0410, Apr. 2018.
[22] S. Klein et al., “Engine in the loop: Closed loop test bench control with
real-time simulation,” SAE Int. J. Commercial Vehicles, vol. 10, no. 1,
pp. 95–105, Mar. 2017.
[23] R. M. Schupbach and J. C. Balda, “A versatile laboratory test
bench for developing powertrains of electric vehicles,” in Proc.
IEEE 56th Veh. Technol. Conf., Vancouver, BC, Canada, Sep. 2002,
pp. 1666–1670.
[24] Z. Filipi et al., “Engine-in-the-loop testing for evaluating hybrid propul-
sion concepts and transient emissions—HMMWV case study,” SAE
Trans., vol. 115, no. 2, pp. 23–41, Apr. 2006.
[25] S. C. Oh, “Evaluation of motor characteristics for hybrid electric vehicles
using the hardware-in-the-loop concept,” IEEE Trans. Veh. Technol.,
vol. 54, no. 3, pp. 817–824, May 2005.
[26] B. Tabbache, Y. Aboub, K. Marouani, A. Kheloui, and
M. E. H. Benbouzid, “A simple and effective hardware-in-the-loop
simulation platform for urban electric vehicles,” in Proc. 1st Int.
Conf. Renew. Energies Veh. Technol., Nabeul, Tunisia, Mar. 2012,
pp. 251–255.
[27] Z. Hu, “Optimization-based robust control for high-performance torque
vectoring in electric vehicles operated by induction traction motors,”
Ph.D. dissertation, IEM, RWTH Aachen Univ., Aachen, Germany,
[28] R. Trigui, B. Jeanneret, B. Malaquin, and C. Plasse, “Performance
comparison of three storage systems for mild HEVs using PHIL sim-
ulation,” IEEE Trans. Veh. Technol., vol. 58, no. 8, pp. 3959–3969,
Jul. 2009.
[29] M. Mudrov, A. Ziuzev, K. Nesterov, and S. Valtchev, “Asynchronous
electric drive power-hardware-in-the-loop system,” in Proc. 17th Int.
Ural Conf. AC Electr. Drives (ACED), Ekaterinburg, Russia, Mar. 2018,
pp. 1–5.
[30] P. Chambon, D. Deter, D. Smith, and G. Bauman, “Electric drive
transient behavior modeling: Comparison of steady state map based
offline simulation and hardware-in-the-loop testing,” SAE Int. J. Pas-
senger Cars-Electron. Elect. Syst., vol. 10, no. 1, pp. 186–193,
[31] K. Etzold et al., “Efficient power electronic inverter control developed
in an automotive hardware-in-the-loop setup,” SAE Tech. Paper 2019-
01-0601, Apr. 2019.
[32] T. Engelhardt, Derating-Strategien für Elektrisch Angetriebene Sportwa-
gen. Wiesbaden, Germany: Springer, 2017.
[33] R. Jayaraman, A. Joshi, V. To, and G. Kaid, “Fidelity enhancement
of power-split hybrid vehicle HIL (hardware-in-the-loop) simulation by
integration with high voltage traction battery subsystem,” SAE Tech.
Paper 2018-01-0008, 2018.
[34] Driver’s Manual Version 6.6.1 IPG Driver, IPG Automot. GmbH,
Karlsruhe, Germany, 2017.
[35] CarMaker Reference Manual Version 6.0.5, IPG Automot. GmbH,
Karlsruhe, Germany, 2017.
[36] J. Wittenburg, U. Wolz, and A. Schmidt, “MESA VERDE—A general-
purpose program package for symbolical dynamics simulations of multi-
body systems,” in Multibody Systems Handbook, W. Schiehlen, Ed.
Berlin, Germany: Springer, 1990, pp. 341–360.
[37] H. Holzmann, K. M. Hahn, J. Webb, and O. Mies, “Simulation-based
ESC homologation for passenger cars,” ATZ Worldwide, vol. 114, no. 9,
pp. 40–43, 2012.
[38] U. Baake, K. Wüst, M. Maurer, and A. Lutz, “Testing and simulation-
based validation of ESP systems for vans,” ATZ Worldwide, vol. 116,
no. 2, pp. 30–35, 2014.
[39] X. Hu, A. Kshatriya, X. Wang, B. Ahrenholz, and S. Folio, “A
thermal electric two-way coupled battery pack model for an all
electric VW motorsport racer,” SAE Tech. Paper 2019-01-0593,
[40] H. Padé, “Sur la représentation approchée d’une fonction par des
fractions rationnelles,” Ann. Sci. l’École Normale Supérieure,vol.9,
no. 3, pp. 3–93, 1892.
[41] O. Wallscheid and J. Bocker, “Derating of automotive drive sys-
tems using model predictive control,” in Proc. IEEE Int. Symp. Pre-
dictive Control Elect. Drives Power Electron. (PRECEDE), Pilsen,
Czech Republic, Sep. 2017, pp. 31–36.
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,
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
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.
... These control strategies reduce or shut down the output torque according to the current motor temperature (Engelhardt, 2017). This limits power significantly since linear derating strategies do not take the thermal dynamics of the electrical machine into account (Etzold et al., 2019). More recently, standard techniques such as proportional integral (PI) or linear-quadratic regulator (LQR) controllers have been implemented to reduce this gap by considering the thermal state of the electrical machine (Wallscheid and Böcker, 2017). ...
... RESULTSThe optimization-based NMPC control strategy is evaluated in terms of a comparison with the aforementioned linear derating approach. InEtzold et al. (2019) an optimal linear derating strategy was identified for the specific PMSM with HiL test bench experiments for the identical use-case. The so-called L125155 strategy operates with a derating start and end temperature of 125°C and 155°C respectively. ...
Full-text available
This paper presents a real-time capable nonlinear model predictive control (NMPC) strategy to effectively control the driving performance of an electric vehicle (EV) while optimizing thermal utilization. The prediction model is based on an experimentally validated two-node lumped parameter thermal network (LPTN) and one-dimensional driving dynamics. An efficient solver for the trajectory tracking problem is exported using acados and deployed on a dSPACE SCALEXIO embedded system. The lap time of a high-load driving cycle compared to a state-of-the-art derating strategy improved by 2.56% with an energy consumption reduction of 2.43% while respecting the temperature constraints of the electric drive.
... The second method is to replace the real-time electric drive model with a physical testing bench. The vehicle model and electric drive controller are compiled and uploaded to real-time simulators, such as Opal-RT [14], Typhoon [15], and dSPACE [16]. During the real-time HIL testing, the vehicle model is solved with CPU. ...
Full-text available
Real-time simulations refer to the simulations of a physical system where model equations for one time-step are solved within the same time period as in reality. An FPGA/CPU-based real-time simulation platform is presented in this paper, with a full-electric vehicle model implemented in a central processing unit (CPU) board and an electric drive model implemented in a field programmable gate arrays (FPGA) board. It has been a challenge to interface two models solved with two different processors. In this paper, one open-loop and three closed-loop interfaces are proposed. Real-time simulation results show that the best method is to transmit electric machine speed from the vehicle model to the electric derive model, with feedback electric machine torque calculated in FPGA. In addition, a virtual vehicle testing tool (CarMaker) is used when building the vehicle model, achieving more accurate modeling of vehicle subsystems. The presented platform can be used to verify advanced vehicle control functions during hardware-in-the-loop (HIL) testing. Vehicle anti-slip control is used as an example here. Finally, experiments were performed by connecting the real-time platform with a back-to-back electric machine test bench. Results of torque, rotor speed, and d&q axis currents are all in good agreement between simulations and experiments.
... Vehicle development is already supported by virtual test environments in many different phases and tasks. Especially in conceptual design and technology selection as well as for On-Board Diagnostics (OBD) verifications, simulation-based test scenarios are used [59,60]. Increasing virtualization is also taking place in the area of drivability [10,61,62] and emissions calibration [63][64][65] to increase the efficiency of the development processes. ...
Full-text available
The combination of different propulsion and energy storage systems for hybrid vehicles is changing the focus in the field of powertrain calibration. Shorter time-to-market as well as stricter legal requirements regarding the validation of Real Driving Emissions (RDE) require the adaptation of current procedures and the implementation of new technologies in the powertrain development process. In order to achieve highest efficiencies and lowest pollutant emissions at the same time, the layout and calibration of the control strategies for the powertrain and the exhaust gas aftertreatment system must be precisely matched. An optimal operating strategy must take into account possible trade-offs in fuel consumption and emission levels, both under highly dynamic engine operation and under extended environmental operating conditions. To achieve this with a high degree of statistical certainty, the combination of advanced methods and the use of virtual test benches offers significant potential. An approach for such a combination is presented in this paper. Together with a Hardware-in-the-Loop (HiL) test bench, the novel methodology enables a targeted calibration process, specifically designed to address calibration challenges of hybridized powertrains. Virtual tests executed on a HiL test bench are used to efficiently generate data characterizing the behavior of the system under various conditions with a statistically based evaluation identifying white spots in measurement data, used for calibration and emission validation. In addition, critical sequences are identified in terms of emission intensity, fuel consumption or component conditions. Dedicated test scenarios are generated and applied on the HiL test bench, which take into account the state of the system and are adjusted depending on it. The example of one emission calibration use case is used to illustrate the benefits of using a HiL platform, which achieves approximately 20% reduction in calibration time by only showing differences of less than 2% for fuel consumption and emission levels compared to real vehicle tests.
Conference Paper
div class="section abstract"> Efficient thermal management is essential in high power density electric drive units (EDUs) due to limited space and working environment. Major heat sources in EDUs are from the inverter, motor and gearbox. System level thermal response prediction models comprising various components within the EDU are of interest from both product performance and software controls standpoint. A system level physics-based lumped parameter thermal network (LPTN) model is built in a one-dimensional (1D) framework using inputs from empirical, electromagnetic, three-dimensional conjugate fluid/heat transfer analysis and test data to predict the component temperature within the EDU. Empirical models were used to calculate heating due to efficiency loss from the gearbox. The thermal loses from the motor are estimated as outputs from electromagnetic simulations. Three-dimensional computational fluid dynamics (CFD) conjugate heat transfer (CHT) simulations were also used at both system and component level to determine heat transfer within the gearbox, motor and inverter. Later due to memory constraints of the microcontroller unit (MCU) the LPTN model is further reduced using a novel temperature estimation based deep neural network to predict component temperatures of interest within the EDU for a given duty cycle. </div
Full-text available
-----------Download-Link: --------------------------- In order to reduce automotive development investments in cost and time for integration, calibration and validation of electric powertrains, particular development tasks are rescheduled to earlier program phases which is usually referred to as frontloading. For frontloading, prototype vehicle tests are shifted to component test benches (road2rig approach). For multiple validation tasks it is crucial that the device under test e.g. an electric drive is tested considering all interactions with neighboring vehicle components. In this contribution, a hardware-in-the-loop methodology is presented with focus on how these interactions can be replicated at component test benches with closed-loop real-time simulations and how electric drives can be calibrated and validated within a virtual vehicle. The hardware-in-the-loop setup is described as cascaded control system considering a multiple input multiple output system. Thereto, the mathematical equations of the multiple input multiple output systems are derived and the cross couplings are analyzed. Due to this analysis, the inner control circuits are set up as decentralized control, which is calibrated considering stability and high dynamics. For the outer control circuits, the simulation models of a battery electric vehicle are developed based on particular measurement data. The device under test consists of an electric drive with a permanent magnet synchronous machine and an inverter. The electric drive is set up at a laboratory test bench and connected with the simulation models to a hardware-in-the-loop setup. The hardware-in-the-loop setup is analyzed considering reproducibility and successfully validated by means of vehicle measurements performed on a chassis dynamometer. Thereafter, parameter variations are conducted for a high load test cycle (Nürburgring Nordschleife) as well as for the Worldwide Harmonized Light Vehicle Test Cycle (WLTC). Based on these parameter variations the significant influence of the interactions between the electric drive and the simulated vehicle components on the driving performance are demonstrated. Finally, an exemplary feasibility study for frontloading of the calibration of electric drives is conducted. Thereto, the driving functions for thermal derating, recuperation and a virtual high voltage dc-dc converter are optimized in terms of available power and energy efficiency as well as successfully validated. These use cases demonstrate the potential of hardware-in-the-loop setups in order to test and optimize electric drives in interaction with the entire vehicle in early phases of automotive development programs.
The application of electric motor emulators (EME) has been prosperous in recent years along with the advancement in power hardware-in-the-loop technology. However, EME design methods based on a typical modeling approach and control structure tend to be invalid in promising a clear, dynamic accuracy objective and a robust dynamic accuracy performance over wide working points, especially at high operating speed. Hence, a new EME design method based on multi-input multioutput (MIMO) modeling and a MIMO closed-loop current control (MCLCC) structure is proposed in this article. Singular values of the MIMO motor model are analyzed to clarify the influence of the rotor speed on the dynamic accuracy objective of EME for the first time, based on which an unknown input observer is designed in the MCLCC structure to compensate for disturbance voltages caused by modeling errors. The transient tracking errors of both current and torque responses can thus be remarkably reduced at 300-Hz electrical speed in the experiment even if 35% modeling errors exist, verifying the superiority of the proposed EME design method in dynamic accuracy.
Multi-motor drivetrain systems utilizing more than one electric machine for propulsion can provide possibilities to improve energy efficiency and vehicle dynamic performance of battery electric vehicles (BEV). However, laboratory testing of such drivetrain systems using a dyno test bench can be costly. A solution can be to use a mechanical-hardware-in-the-loop (MHIL) test bench, which combines real-time simulations of the intended working environment with the dyno test bench. To utilize the MHIL approach for multi-motor drivetrain systems, one drivetrain is implemented in the dyno test bench, while the remaining are simulated using a real-time simulator. Therefore, providing a less expensive solution for laboratory testing of drivetrain components and control methods in their intended environment. In this work, an MHIL test bench for a multi-motor drivetrain system is designed and experimentally verified. A BEV with two independently driven front wheels is considered for modeling. To interface the dyno test bench with real-time simulation, two different methods, namely open-loop and closed-loop, are proposed and verified in experiments by prototyping an MHIL test bench. In addition, an anti-slip control is implemented and evaluated experimentally to demonstrate the suitability of the proposed MHIL test bench in the verification of control methods.
System-level thermal modelling of electric drive systems requires simple thermal models coupled with each other by the operation parameters of the drive systems. This paper presents a simple parameterized power loss model for insulated gate bipolar transistor (IGBT) inverters, in which variables are relevant to the powertrain operation conditions and are routinely monitored. Combined with a two-impedance thermal model of the inverter with parameters fitted to the results of computation fluid dynamics (CFD) simulations, the resulted real-time model can provide accurate estimations of the junction temperature. The present power loss model combined with the simple two-impedance thermal model is very suitable to be used in system-level real-time thermal monitoring of an integrated electric drive system.
Full-text available
This article presents a spatial harmonics model of a permanent magnet synchronous machine implemented on a field-programmable gate array (FPGA). The real-time model is parameterized by finite element analysis (FEA) and is suitable for model-based development and hardware-in-the-loop (HIL) applications. Since the quality of the real-time model depends on the parameterization, the accuracy of three FE models of different fidelity levels is evaluated by test bench measurements. It was found that a priori assessment can be realized with good precision by virtual prototypes. In addition to the selection of a suitable FE model for parameterization, a surrogate model to describe the machine effects is mandatory to achieve real-time capability. Using an inverse current-flux correlation in the dq reference frame and a parallel resistor in the equivalent circuit for iron loss consideration, a high-fidelity model is developed. Moreover, the temperature effect is solved by a highly sophisticated shifting strategy of the reference currents to affect the nonlinearity and harmonics of the electromagnetic behavior. The temperature correction approach yields a maximum deviation of 0.3 Nm. This corresponds to a percentage error of less than 1%.
Full-text available
Embedded systems encompass software and hardware components developed in parallel. These systems have been the focus of interest for many scholars who emphasized development issues related to embedded systems. Moreover, they proposed different approaches for facilitating the development process. The aim of this work is to identify desirable characteristics of existing development methodologies, which provide a good foundation for development of new methodologies. For that purpose, systematic mapping methodology was applied to the area of embedded systems, resulting in a classification scheme, graphically represented by a multilayer conceptual network. Afterwards, the most significant clusters were identified, using the k-means algorithm and the squared Euclidean distance formula. Overall, the results provide guidelines for further research aiming to propose a holistic approach for the development of special case of embedded systems.
Conference Paper
Full-text available
Due to the increasing concerns on energy and environmental issues, the automotive industry has seen increased growth and development of electric and electrified vehicles. The power-split design is one of the most common drivetrain configurations of a hybrid or electrified vehicle. The propulsion system of a power-split hybrid vehicle typically comprises of an engine drive system in which the engine, drivetrain and generator are mechanically coupled on a planetary gear set driveline while the electric drive system consists of a high voltage battery and a traction motor. In recent years, Hardware-in-the-Loop (HIL) simulation has become an increasingly common approach for controls rapid prototyping and validation as part of the automotive product development cycle. Traditionally, HIL simulations of hybrid vehicle controls and high-voltage battery controls have been implemented on separate HIL benches which are exclusively targeted for hybrid vehicle controls and battery controls simulations respectively. This research demonstrates an implementation of enhanced fidelity of a power-split hybrid vehicle powertrain controls HIL by integrating it with high-voltage traction battery subsystem HIL by networking the two aforementioned HIL systems together. The power-split hybrid vehicle HIL typically use simplified battery plant and controller models, and therefore, the addition of the high-voltage battery HIL provides a more detailed simulation of the high-voltage battery in which each cell is modeled such that cell voltage varies based on initial State-of-Charge (SOC) and temperature, capacity, fan speed, self-discharge, and other chemistry-based parameters. The integration of the battery HIL also provides the high-voltage interface to the battery controller hardware. The 2017 Ford Fusion Hybrid is used as the platform for this research. The battery subsystem performance of the vehicle is used as the baseline for comparison between the battery subsystem performances of the simplified power-split hybrid vehicle HIL and the networked HIL setup to understand the increased fidelity and accuracy of the latter.
Conference Paper
New 12V/48V power net architectures are potential solutions to close the gap between customer needs and legislative requirements. In order to exploit their potential, an increased effort is needed for functional implementation and hardware integration. Shifting of development tasks to earlier phases (frontloading) is a promising solution to streamline the development process and to increase the maturity level at early stages. This study shows the potential of the frontloading of development tasks by implementing a virtual 48V mild hybridization in an Engine-in-the-Loop (EiL) setup. Advanced simulation technics like Functional Mock-up Interface (FMI) based co-simulation are utilized for the seamless integration of the real time simulation models and allow a modular simulation framework as well as a decrease in development time. As base line, an existing and validated co-simulation consisting of a GT-POWER engine model, a SimulationX transmission model, and a dSPACE ASM vehicle dynamics model is used. A Simulink-based dual 12V/48V power net model is developed to extend the base model. The 48V side is mainly composed of a belt driven starter generator (BSG) that is directly connected to the combustion engine (P0-layout) and a 48V Li-Ion battery. The 48V side is coupled via a bidirectional DC/DC to the 12V Absorbent Glass Mat (AGM)- battery and the 12V loads. In the next step, an engine test bench is coupled with the real time simulation by replacing the simulated combustion engine. Extensive tests are carried out on the Engine-in-the- Loop test bench, considering new legislative test requirements like WLTC (Worldwide harmonized Light vehicles Test Cycle) and RDE (Real Drive Emission). The results show the great emission reduction potential of 48V mild hybrids and proof that the frontloading-based EiL methodology is a promising solution to validate the system behavior with a heterogeneous cyber-physical test setup.
Conference Paper
This paper describes an approach to reduce development costs and time by frontloading of engineering tasks and even starting calibration tasks already in the early component conception phases of a vehicle development program. To realize this, the application of a consistent and parallel virtual development and calibration methodology is required. The interaction between vehicle subcomponents physically available and those only virtually available at that time, is achieved with the introduction of highly accurate real-time models on closed-loop co-simulation platforms (HiL-simulators) which provide the appropriate response of the hardware components. This paper presents results of a heterogeneous testing scenario containing a real internal combustion engine on a test facility and a purely virtual vehicle using two different automatic transmission calibration and hardware setups. The first constellation is based on an already validated vehicle model (A), including a physical dual-clutch transmission model (DCT), a semi-physical tire model and a vehicle dynamics model. With this standard configuration, the real-time model accuracy is initially illustrated by comparing the operating points distribution and the tailpipe emissions (diluted vs. undiluted) in “Worldwide harmonized Light vehicles Test Cycle” (WLTC) tests for the closed-loop setup at the engine test bench to the real vehicle on a chassis dynamometer. Furthermore, the achievable reproducibility with this in-the-loop approach regarding gaseous and particulate emissions is shown. Finally, the sensitivity and reproducibility of tailpipe emissions related to changes in the calibration set of the virtual “Transmission Control Unit” (TCU) are pointed out for this configuration in “Real Driving Emissions” (RDE) tests. In a second step, another vehicle model (B) is set up and also validated using extensive vehicle measurements. In contrast to model A, model B is equipped with an eight speed automatic transmission model, based on physical relations and an all-wheel drive drivetrain model. During the validation process of model B, several drivability and emission tests have been performed in a Model-in-the-Loop simulation environment. Afterwards, the validated transmission and TCU models were virtually installed into the vehicle model A, resulting in vehicle variant C. This physically nonexistent, virtual vehicle was then tested at the Engine-in-the-Loop test facility. The conceptually different results at the test bench are compared and discussed regarding the vehicle A setup. The potential and reproducibility of the Engine-in-the-Loop approach are shown by a compilation of the results for the variants A and C.
Virtual system integration and testing using Hardware-in-the-Loop (HiL) simulation enables frontloading of development tasks, provides a safer and reliable testing environment and reduces prototype hardware costs. One of the greatest challenges to overcome when performing HiL simulations is assuring a high model accuracy under stringent real-time requirements with acceptable development effort. Instead of being developed from scratch, this work shows that plant models suitable for HiL implementation can be derived directly from the detailed models already available from the component layout phase. This is possible by using a seamless simulation toolchain and co-simulation methodologies throughout the development process. In this paper, a detailed one-dimensional (1D) GT-POWER model for a state-of-the-art turbocharged diesel engine with exhaust gas recirculation (EGR) is simplified and transformed to a HiL platform connected to an engine control unit (ECU). Although the air path is reduced to zerodimensional (0D) volumes to fulfil the real-time requirement, the engine model remains semi-physical and crank angle resolved. The major pressure pulsations within the system are well captured, which is mandatory for the determination of volumetric efficiency, turbocharger operation and EGR distribution. A predictive combustion model based on injection profiles is implemented for modelling of the indicated engine efficiency and the exhaust gas temperature. After detailed investigations on steady-state and transient model performance in an offline environment, the model is integrated into the HiL testing platform. The coupling of the model to the ECU interface has been implemented using the co-simulation approach on FEV’s xMOD platform. The simulation results of the integrated HiL system, including the engine thermodynamics and the controller behaviours, have been validated with measurement data from engine test bench and the real-time capability of the model has been proven. The work has demonstrated the capability and advantages of a seamless transfer from component design to system integration and testing within a combustion engine development process.
Conference Paper
Die Komplexität von Antriebssträngen nimmt im Zuge der Elektrifizierung stark zu. Ebenso steigen die Aufwände für Integration, Validierung und Erprobung der neuen Antriebsstränge. Um Zeit und Kosten zu reduzieren, werden Entwicklungstätigkeiten in frühere Projektphasen verschoben. Im Rahmen dieser Vorverlagerung, des sogenannten Frontloadings, werden vermehrt Prüfstände an Stelle von Prototypenfahrzeugen verwendet (Road2Rig-Ansatz). Bei herkömmlichen Prüfstandtests werden einzelne Komponenten losgelöst vom gesamten Fahrzeug untersucht. Wechselwirkungen zwischen Fahrzeugkomponenten können in der Regel nur unzureichend abgebildet werden. Bei der Auslegung des Gesamtfahrzeugs werden diese Wechselwirkungen jedoch simuliert, sodass herkömmliche Prüfstandtests für die Systemvalidierung unzureichend sind. Beim Closed-Loop-Testing ist es jedoch möglich, Prüfstände mit Echtzeitsimulatoren zu koppeln und während der Tests einzelner Realkomponenten die Wechselwirkungen durch Co-Simulation des gesamten Fahrzeugs zu berücksichtigen. In diesem Paper wird ein Closed-Loop-Testansatz anhand eines Fallbeispiels, der Elektrifizierung eines A-Segment-Fahrzeuges, beschrieben. Das Fahrzeug ist in der Ursprungskonfiguration mit einem Verbrennungsmotor und Doppelkupplungsgetriebe ausgerüstet. Eine zweite Testkonfiguration beinhaltet einen Elektromotor, der über eine feste Getriebestufe mit der Hinterachse verbunden ist. Für die Ursprungskonfiguration werden umfangreiche Testergebnisse ermittelt, welche für die Kalibrierung eines dSPACE ASM Fahrzeugdynamikmodells verwendet werden. Dieses Fahrzeugdynamikmodell, der Prüfstand mit Automatisierungssystem sowie ein dSPACE HiL Scalexio werden zu einer Engine-in-the-Loop-Testumgebung zusammengeführt. Abschließend werden für die Testkonfigurationen sowohl mit Verbrennungs- als auch mit Elektromotor ein RDE-Testzyklus gefahren und beide Antriebsstrangkonzepte hinsichtlich Energieverbrauch und transienter Verläufe relevanter Messgrößen verglichen.