Download Design optimization of traction applications PL CN66

CEDRAT News - N° 66 - June 2014
Design optimization of traction motors for EV
applications.
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I
ncreasing concerns on energy security and environmental
pollution by fossil fuel engines have pushed significant
research in electric vehicles (EVs). The requirements for traction
electrification are highly demanding in terms of efficiency, torque
and power density, wide constant power operating range and
cost effectiveness.
The challenge for the EV traction machine design is that it has
to produce high torque, usually 2-3 times the nominal value, at
standstill or low speed in order to provide the required acceleration
and hill climbing requirements. On the other hand, the machine
needs to output peak power (~2x the nominal value) over a wide
speed range to provide acceleration during high speed driving.
This wide torque-speed operating range imposes significant
constraints on the achievable machine efficiency and power
density using conventional design methodologies. Since an EV
operates over a wide torque-speed range in response to various
dynamic driving conditions, the traction machine design should
be aimed to achieve overall energy saving under a given set of
constraints over a representative driving cycle rather than a few
operating points (such as the rated and/or peak operating points
at base and/or maximum speed which are mainly used for hillclimbing/acceleration during driving).
In order to realize high fidelity multi-parameter FE-based design
optimizations against driving cycles, while avoiding enormous
computational time and maintaining high accuracy, two
solutions were exploited. The driving cycles, such as the New
European Driving Cycle (NEDC) and Artemis Urban Driving Cycle
(AUDC) shown in Fig. 1, are characterized into a finite number
of representative points (referred to as “energy gravity centers”),
which can accurately represent the cycle energy loss (viz. the
error being <4%). The “energy gravity centers” for NEDC and
AUDC are illustrated in Fig. 2 under the required Torque-Speed
envelope of the motor. Thus, the optimization can be conducted
against these operating points leading to a drastically reduced
computational time. Further, the use of an efficient FE-coupled
optimization tool (GOT-It) with distributed computing capabilities
(CDE) provides an ideal and effective optimization environment for
EV traction machines. It can yield a global optimum solution with
the minimum number of FE evaluations (i.e. dependent on the
number of optimizing parameters) without sacrificing accuracy.
Accordingly, the development of effective and computationally
efficient optimization techniques and tools is considered essential
for the design of EV traction machines. In this article, the process
of enhancing the energy efficiency of a traction machine against
a defined driving cycle with the aid of CEDRAT’s software suit
(i.e. Flux, GOT-It and CEDRAT distribution engine - CDE) will be
demonstrated.
Design specification, challenges
and approach
The integration of driving cycles in the design optimization process
places significant challenges in terms of computational time,
especially when high fidelity FE evaluations against the hundreds
of cycle points are required.
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Parameter
Value
Active length
105 mm
Stator outside diameter
2100 rpm
Maximum cruise speed
8200 rpm
Peak torque below and at base speed for 120 s
30.0 Nm
Continuous torque below and at base speed
17.0 Nm
Peak torque at maximum cruise speed
7.4 Nm
Continuous torque at maximum cruise speed
4.4 Nm
Peak power
6.6 kW
Continuous power below and at base speed
3.75 kW
Nominal DC link voltage
120 V
Maximum inverter current
120 A
Continuous current density - Jrms
Maximum permissible line-to-line voltage
Cooling method
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120 mm
Based speed
< 8 Arms/mm2
< 250 V
Air-cooled
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(see continued on page 7)
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CEDRAT News - N° 66 - June 2014
GOT-It
GOT-It & CDE
Optimizations using GOT-It & CDE
The motor considered for the purposes of this article is part of a
distributed drive-train, class 2 EV. The traction machine design
specification, obtained from the vehicle’s dynamic model and
acceleration and hill-climbing requirements, are listed in Table 1.
An interior mounted permanent magnet synchronous motor
(IPMSM) was used as a design case study to demonstrate the
optimization process. The motor cross section along with its
leading design parameters is shown in Fig. 3. The motor was
optimized with respect to 8 design parameters, viz. the back-iron
thickness (Hy), tooth width (Tw), rotor to stator-outer radius ratio
(Ri/Ro), magnet depth (Dm), thickness (hm), width (wm), angle span
(βm) and coil turn number (TN). The multi-parameter, FE-based
optimization process with Flux and GOT-It is illustrated in Fig. 4.
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The objective was to maximize the system (motor + inverter)
energy efficiency over the NEDC while satisfying the required
torque-speed operating range and a given set of electrical,
thermal and volumetric constraints (viz. Table 1). The use of a
sequential surrogate optimizer (SSO) from GOT-It, which enables
indirect optimization through response surface building and postprocessing by genetic algorithm (GA), allows for timely efficient
multi-parameter optimizations. For this case, where 8 parameters
are involved, 246 design samples should be solved in 3 iterations
in order to acquire a global optimum solution with the SSO. This
approximates to a computational time of 123 hours, since it takes
~30min to evaluate the system efficiency from the FE model for
each sample over the 12 NEDC representative points.
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Whilst this is far superior to simple parametric optimizations, it
can still be considered as relatively computationally intensive,
particularly if multiple optimizations or more optimizing
parameters are required. It should be noted that the computational
time per sample varies depending on the model size and nonlinearity. In this case, only 1/6th of the model was considered due
to symmetry. This implies a 3-fold increase in computational time
(~ 15 days) for a case where half model must be used.
However, the computational time for large multi-parameter
optimization models can be significantly further reduced by
using a distributed computing tool (CDE) associated with GOT-It
optimizer. This allows for multiple design samples to be solved
simultaneously on a multi-core CPU as illustrated in Fig. 5.
Compared to sequential computing (123 hours), the global
optimum solution that satisfies all design constraints is acquired
in only 35 hours for the 8-parameter optimization problem. The
system energy efficiency evolution over NEDC is shown in Fig. 6.
The reduction in computational time is approximately linear to
the number of CPU cores used (in this case 4). This indicates that
the 35 hours can potentially further reduce to almost half (~ 17
hours) if an additional 4-core CPU is used in a cluster configuration.
Conclusion
It has been shown that multi-parameter, high fidelity FE-based
optimizations against driving cycles, which typically require an
enormous amount of computational time, can be effectively
performed in a drastically reduced time by using GOT-It and CDE.
This provides great versatility in rapidly exploiting the complete
design space for EV traction machines which are characterized by
many design challenges.
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