Download Real-time Virtual Test-bench for Electric Vehicle Propulsion Systems

The 18th National Conference on Electrical Drives “CNAE 2016”
Real-time Virtual Test-bench for Electric
Vehicle Propulsion Systems
Sebastian Vasile Ciceo1, Hunor Nagy1, Mircea Ruba1, Claudia Martis1,
Horia Hedesiu1
1
Faculty of Electric Engineering, Technical University of Cluj-Napoca, Romania
Abstract - This paper presents a virtual test-bench for the evaluation of two drivetrains for electric vehicles, consisting of a
switched reluctance and a synchronous reluctance motor drive, simulated in real-time on FPGA, coupled via CAN bus with a
forward-facing vehicle simulation model. In this way control algorithms can be assessed with results showing up in the vehicle's
behaviour, providing a high fidelity hybrid method that combines rapid control prototyping with a real-time simulated plant model.
Keywords – Rapid Control Prototyping, Electric Vehicle, Synchronous Reluctance Motor, Switched Reluctance Motor, Real-Time
Simulation
1.
INTRODUCTION
The increasing shift in the automotive market
towards electric powertrains and intelligent vehicles
introduces the need of developing and testing
mechatronic systems in a time and cost saving fashion
[13]. By integrating the control and plant modelling
from an early design stage in an virtual environment
under the paradigm of Model-based System
Engineering (MBSE), thus using a common design and
validation environment available for different
engineering domains, design and integration problems
can be avoided efficiently [2], [13].
The conventional “V” development cycle used for
automotive applications incorporates the following
stages [3], [4]:
Model-In-the-Loop (MIL) simulation were the
plant and controller models are running together offline
in order to validate the control algorithm or the system
model.
Software-In-the-Loop (SIL) were actual code is
tested under the simulated plant model in order to
validate code implementation.
Hardware-In-the-Loop (HIL) simulation were
physical hardware is tested in the presence of a realtime plant model in order to validate the hardware
interactions with the plant.
Rapid Control Prototyping (RCP) where closedloop tests are conducted using a physical plant and a
prototype controller in order to assess the controller
behaviour.
This paper proposes a Model-based System
Testing method (MBST) [5] where we combine the
advantages of RCP with those of HIL simulation in the
field of EV traction application. By coupling an FPGA
Fig. 1 Virtual Test-bench system overview
ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637
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The 18th National Conference on Electrical Drives “CNAE 2016”
based prototype controller using RCP and a real-time
compatible virtual EV system plant model, the control
algorithms can be tested in real-time before the physical
test-bench is built and even before the final system
model is fully configured, for example the final 3D
motor model can be still under development [6].
By integrating multiple simulation and
prototyping tools into a virtual test-bench where
different subsystems depending on their physical
domain (having different dynamic behaviour) can run at
different time-rates, we are able to close the gap
between testing and simulation. [2], [5].
Our focus is on decoupling the control and
analytical model of two reluctance based electric
machines drives [8], [9], [12] from the system model of
the EV in order to test the real-time control algorithms
in a safe way and check the interactions with the system
model. By using this approach, extreme conditions and
faulty behaviour can be tested without the risk of
damaging physical equipment [2]. Because the virtual
test-bench is easily reconfigurable, it is possible to
compare different traction drive topologies and control
algorithms and assess their performance behaviour at
the system level.
2.
METHODOLOGY
2.1. System overview
The challenges of real-time simulation for EV
traction drives consist in accurately representing fast
non-linear transient response in discrete time domain,
implying the need of a small integration step size. This
occurs because the electrical system has a higher
bandwidth compared to thermal or mechanical systems
[6], [2]. In order to bypass this issue, some marketavailable solutions (discrete-time compensation
methods) use interpolation algorithms and time
stamping [2].
The solution adopted in this paper is using RCP
for the controller and analytical representation of the
power converter and machine model in NI LabVIEW
FPGA Module implemented on dedicated prototyping
hardware: NI PXI industrial controller with an R Series
FPGA module. This removes the time-demanding task
of programming in hardware description language and
ensures a clock rate of above 1 MHz which is sufficient
for ultra-fast transients [2].
The EV system model is designed in LMS
Imagine.Lab Amesim – an integrated simulation
platform for multi-domain mechatronic systems
simulation, compiled as a Dynamic-link library (DLL)
and integrated into NI VeriStand - a software
environment for configuring real-time test applications.
It runs on a second PXI controller that is able to run the
EV model at a clock rate above 10 kHz, depending on
the system model complexity.
The communication between the electric drive
real-time model and the EV real-time model is done via
a CAN Bus interface, using the NI XNET API running
on the real-time controller of the PXIs, ensuring
deterministic data transfer up to 1 Mb/s.
In this paper two electric drives using machines
working on the principle of magnetic reluctance have
been studied: the synchronous reluctance motor
(SynRM) and the switched reluctance motor (SRM)
(Fig. 1).
2.2. SRM model and control
The SRM has a simple doubly salient structure,
which gives an advantage in construction costs, with a
working principle based on magnetic reluctance. Thus
by applying voltage sequentially on its phases,
magnetic flux is generated according to the rotor
position and the current excitation. However due to the
operating principle and structural characteristics the
resulting magnetic saturation, flux linkage and
generated torque have are highly non-linear [11].
( ,)
(1)
= +
( , )=
=
( , )
(2)
, where V represents the supply voltage, R is the
phase resistance, i the phase current,
is the flux
linkage,
is the rotor position and T is the torque
produced by the co-energy variation caused by the rotor
movement [10].
Fig. 2 Current in function of rotor position and flux linkage
When designing the control of the machine the
described non-linarites have to be taken into account. In
order to define the flux linkage and the output torque,
estimation methods have been proposed in the literature
[14]. A hybrid model was chosen, where the magnetic
characteristics are defined beforehand by FEM
simulations and stored in look-up tables (LUT) (Fig. 2)
while the rest of the model is defined by analytical
expressions, resulting in a fast and precise model at the
cost of higher memory storage needed for FPGA
implementation, thus being a superior approach to motor
modeling [1].
The SRM drive consists of the before mentioned
machine model based on LUTs, controlled by a
hysteresis current controller using an asymmetric halfbridge converter. Positive, negative or zero voltage is
applied on the machine phases, the values being a
function of the firing angles compared to rotor position
and current reference compared to a hysteresis
bandwidth.
ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637
The 18th National Conference on Electrical Drives “CNAE 2016”
Fig. 4 Control diagram of the SynRM
Fig. 3 Control diagram of the SRM
The current reference input to the controller is
calculated by a PI controller, relying on the error
between the torque input and actual, computed torque
(Fig 3).
2.3. SynRM model and control
The SynRM has a three phase stator with
distributed windings and an anisotropic rotor with flux
barriers serving a saliency ratio between d-axis and qaxis inductances (Ld and Lq). Having these mechanical
characteristics the rotor synchronously follows the
rotating magnetic field in the air-gap, based on the
principle of minimal reluctance.
The governing equations of the machine defined
in conventional dq frame are as following:
=
+
=
+
=(
(3)
+
)
361
(4)
(5)
,where V represents the applied voltage,
stator
phase resistance, L is the inductance, and i the current,
with d and q marking the d- and q-axis orientation,
while ω the angular velocity of the rotor.
The applied control method is based on the field
oriented control (FOC) method, with the dq current
references being supplied by PI controllers, and by
using the inverse Park transformation, voltage is
applied by a three phase inverter controlled by a PWM
controller (Fig. 4).
2.4. EV system model
The EV model is a simple forward-facing vehicle
model designed in LMS Imagine.Lab Amesim. The
reason behind the model design simplicity is that it was
built only in order to demonstrate the platform
capabilities (more complex systems designs serving
different purposes, such as energy consumption or
driveability can be added later).
The EV consists of a driver model (PI controller)
that sends acceleration and braking commands to the
Vehicle Control Unit (VCU), which is computing and
sending the desired torque reference to the VeriStand
communication block that interfaces the electric drive.
2.5. LabVIEW FPGA implementation
Due to the high bandwidth required by the
electrical loop because of the hysteresis controller (Fig.
5) and cascaded control structure, where the need of
knowing the variables, in every iteration of the control
program, NI LabVIEW FPGA Module has been used.
For control purposes, the characteristics of the
current as a function of flux linkage and rotor position
( ( , )) are needed together with the torque as a
function of rotor position and current amplitude
(T( , )). Therefore, data representing these variables
have been stored as LUTs in the block memory of the
FPGA as long data arrays. Accessing the elements from
the memory was done by calculating the address in
function of the two input variables. These are linearly
increasing arrays in a LUT therefore, by dividing the
input elements with the slope of their series, the
queuing address can be calculated.
Fig. 5 Hysteresis current control implemented in LV FPGA
ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637
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The 18th National Conference on Electrical Drives “CNAE 2016”
By adopting this method the current and torque data can
be acquired at every iteration in 18.75ns.
The control algorithm was implemented by
carrying out operations in Single Cycled Timed Loops
(SCTL) having the advantage of minimizing the
resource usage of the FPGA and increasing the
execution speed. This is achieved because the
calculations done in SCTLs store the logic blocks and
the connection between them, in the FPGA hardware
thus eliminating the need for using registers. Timing of
the control algorithm was achieved by the use of a
clock based counter, and setting the step size according
to the sampling frequency.
Differential equations were solved using the
discrete Forward Euler method, while data operations
were conducted by using fixed-point representation,
where output precision is determined by setting the
word and integer word length of every single operation
in order to achieve a proper balance between precision
and FPGA resource exploitation.
Communication between the FPGA target and the
real-time target has been done via read/write protocols,
by which I/O can be written and read deterministic.
3.
analytical models of the machines with full FEM
models, as showed in [6].
ACKNOWLEDGMENT
This work was supported by UEFISCDI PCCA
181/2012 ALNEMAD.
REFERENCES
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5.
RESULTS AND CONCLUSIONS
In order to validate the virtual test-bench, the first
600 seconds of a reduced scale New European Drive
Cycle (NEDC) was given as test stimuli.
Fig. 6 shows the output torque for the two
simulated drives with their respective reference given
by the VCU, during the first 200 seconds of the
simulated run.
6.
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9.
10.
Fig. 6 Estimated torque for the SRM and SynRM during the
imposed drive cycle
As a conclusion, a virtual test-bench having the
purpose of testing the interaction of control algorithms
of electric drives with a simulated system model of an
EV in real-time, thus combing the advantages of using
RCP and HIL simulation. By adopting the FPGA
implementation for the control and electric drive model
numerical instabilities occurring in real-time are
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As a future development, it is possible to validate
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them in a mechanical level HIL setup. Also, it is
possible to refine the virtual test-bench by replacing the
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ACTA ELECTROTECHNICA, Volume 57, Number 3-4, 2016, Special Issue, ISSN 2344-5637