Challenges Faced by Electric Vehicle Motors and Their Solutions

Abstract

This paper reviews motor techniques for reducing the cost of electric vehicles (EVs) and improving their range. In recent years, environmental issues, energy crises and the greenhouse effect have urged the popularization of clean energy EVs. In order to achieve this goal, it is necessary to overcome technical difficulties in vehicle cost and range. As a key component of an EV, the motor occupies a large proportion of the overall vehicle cost, and its efficiency directly affects the mileage. In this context, this article discusses the merits and challenges of three mainstream EV motors: permanent magnet synchronous motor (PMSM), induction motor (IM), and switched reluctance motor (SRM) in terms of vehicle cost and range. Then this paper compares the advanced techniques of these motors in terms of topology, material applications and control strategies. Finally, the development trends and opportunities of the three motors in EVs are predicted.

Full text

Received November 29, 2020, accepted December 13, 2020, date of publication December 18, 2020,
date of current version January 11, 2021.
Digital Object Identifier 10.1109/ACCESS.2020.3045716
Challenges Faced by Electric Vehicle Motors
and Their Solutions
ZHIKUN WANG1, TZE WOOD CHING 2, (Senior Member, IEEE), SHAOJIA HUANG3,
HONGTAO WANG 1, (Member, IEEE), AND TAO XU 1
1Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
2Faculty of Science and Technology, University of Macau, Macau, China
3Faculty of Mechanical Engineering, Zhuhai College of Jilin University, Zhuhai 519041, China
Corresponding authors: Tze Wood Ching (twching@ieee.org) and Tao Xu (wanxiao0756@gmail.com)
This work was supported in part by the Startup Funds for Scientific Research of High-Level Talents of Wuyi University under
Grant 2019AL020, in part by the Jiangmen Science and Technology Project under Grant 2020030102220005387, in part by the Off
Campus Practice Teaching Base for College Students of Zhuhai College of Jilin University under Grant ZLG20180703, in part by the
Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province under Grant 2020ZDZX3018,
and in part by the Science Foundation for Young Teachers of Wuyi University under Grant 2018td01.
ABSTRACT This paper reviews motor techniques for reducing the cost of electric vehicles (EVs) and
improving their range. In recent years, environmental issues, energy crises and the greenhouse effect have
urged the popularization of clean energy EVs. In order to achieve this goal, it is necessary to overcome
technical difficulties in vehicle cost and range. As a key component of an EV, the motor occupies a large
proportion of the overall vehicle cost, and its efficiency directly affects the mileage. In this context, this
article discusses the merits and challenges of three mainstream EV motors: permanent magnet synchronous
motor (PMSM), induction motor (IM), and switched reluctance motor (SRM) in terms of vehicle cost and
range. Then this paper compares the advanced techniques of these motors in terms of topology, material
applications and control strategies. Finally, the development trends and opportunities of the three motors in
EVs are predicted.
INDEX TERMS Electric vehicles, EV motor, control strategy, motor topology development.
I. INTRODUCTION
In the past ten years, environmental problems caused by
many greenhouse gas emissions have become increasingly
serious, which has promoted countries to pay more attention
to energy conservation and emission reduction. Transporta-
tion is one of the largest contributors of greenhouse gas
emissions: it accounts for approximately 27% of the total
emissions [1]. However, fuel vehicles are still the main com-
ponent of the transportation system [2]. Due to the develop-
ment of batteries, and the desire to reduce greenhouse gas
emissions and improve urban air quality, the electric vehi-
cle (EV) manufacturing industry has begun to receive atten-
tion from governments. Compared to internal combustion
engine vehicles (ICEVs), the benefits of EVs include zero
exhaust emissions, higher efficiency, and the vast potential
for reducing greenhouse gas emissions combined with the
low-carbon power sector. In this context, many countries
have successively announced the goal of achieving 100%
The associate editor coordinating the review of this manuscript and
approving it for publication was Jun Shen .
zero-emission vehicles or phasing out ICEVs by 2050, and
proposed incentives for EV purchase and production to sup-
port the development of the EV industry. However, in 2019,
the number of available EVs was 7.2 million, which is only
approximately 1% of the global car inventory [3]. Therefore,
the popularization of EVs still has a long way to go.
According to the survey results of [4], [5], the main
obstacle to the use of the EV is the concern about mileage.
Drivers do not need to worry about the availability of gas
stations when using ICEVs but need to plan their trips to
avoid insufficient power before reaching the charging station.
On the other hand, the higher expenditure for the purchase of
EVs also reduces the adoption of EVs. Considering policy
subsidies, the total cost of EVs is slightly lower than that of
ICEVs [6]. However, government subsidies for supporting
EV purchases are short-lived. When sales increase by a cer-
tain amount, the subsidies will be canceled. In the absence
of subsidies, the cost of EVs is difficult to compete with
ICEVs with current EV techniques and manufacturing scale
and thus hindering the continued growth of EV sales. The
main reasons that hinder the popularization of EVs can be
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Z. Wang et al.: Challenges Faced by EV Motors and Their Solutions
attributed to short mileage and the high cost of EVs. These
problems are closely related to the performance and cost of
the motor system. The mileage is directly affected by the
efficiency of the motor and power/torque density, and the cost
of the motor is second only to the cost of the battery [7].
However, as far as the author knows, there is currently no
published article comprehensively discussing motor design
and control methods for low-cost, long-range EVs.
In this context, this article reviews the challenges faced by
different EV motors to achieve low-cost, long-range EVs, and
current solutions to overcome them. Next, the second part of
the article investigates the merits and drawbacks of various
current EV motors including PMSMs, IMs and SRMs, and
lists their shortcomings that hinder the popularization of EVs.
The third to the fifth part of the article sequentially compares
the defect solutions of the three EV motors.
II. CURRENT CHALLENGES OF EV MOTORS
We investigate the EV market from 2010 to 2020 and show
motors used in some EV models in Table 1. We conclude
that the motors that are currently installed in EV propul-
sion systems mainly consists of the PMSM, IM, and SRM.
Among them, the PMSM has become the first choice of
EV manufacturers due to the high torque and high power
density enabled by high-energy-density PMs (neodymium Fe
boron (NdFeB) and samarium cobalt (SmCo)) [8]–[11]. The
PMSM is divided into the internal-PMSM and the surface-
PMSM. With the same size, the overload capacity of the
internal-PMSM is better [9], so the internal-PMSM is more
common in EVs. Because the high-energy-density PM is
affected by low yield and is non-renewable and geopolit-
ical, its cost is at least twice than the total cost of other
raw materials of electric motors [12]. Therefore, techniques
that can reduce PM costs without substantially sacrificing
performance are urgent and important for the EV indus-
try. The permanent magnet assisted synchronous reluctance
motor (PMasyRM) and spoke-type motor are low-cost solu-
tions for PM motors.
Among PM-free motors, the IM has successfully pene-
trated the EV market with its mature techniques and low-cost
advantages (Table 1). However, due to the limitation of its
structure, the efficiency performance of the IM is worse
than other motors [11], [13], which is not conducive to
EV mileage. It is foreseeable that with the development
of other motor techniques, the cost advantage of mature
IM technology will be gradually reduced. For example, the
SRM with the lowest material cost is receiving an increasing
amount of attention [12]. To achieve the same output power
(30kW), the material cost of SRM is approximately half of
the PMSM (NdFeB) and less than 80% of that of the IM.
However, considering the shortcomings of low torque density,
high torque ripple, and high noise, few EVs currently use
SRM (Table 1).
In addition, because the regenerative braking technique
can recover electrical energy for long mileage, this technique
is becoming increasingly important for EVs. Improving the
TABLE 1. Example of EVs on the market from 2010 to 2020, including
their model, motor categories, and power.
motor-related performance to increase the energy recovered
by regenerative braking has become a new challenge for EV
motors. According to the analysis of [14]–[17], the energy
recovered by braking is proportional to the torque and motor
efficiency, and the effect of regenerative braking is best in
the constant power region close to the base speed. There-
fore, to improve the energy recovery effect of regenerative
braking, the EV motor needs to have as wide a constant
power range as possible in addition to the highest efficiency.
Based on motor design research [18]–[21], it can be seen
that the PMSM with its high efficiency and wide constant
power range is the most suitable EV motor for regenerative
braking. The energy recovery rate of the IM is affected by low
efficiency, which also reduces the output power, and shortens
the constant power range. Although the SRM has a wide
speed range due to its excellent high-temperature resistance
and reliable mechanical structure, its poor torque capacity
limits the energy recovery effect.
The advantages of PM motors are high efficiency, high
torque density, and suitability for long-range EVs. The expen-
sive cost of the PM is a challenge to overcome. In PM-free
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TABLE 2. Price and properties of PM materials [22].
motors, the IM cost and torque density are moderate whereas
its low efficiency is a disadvantage. The cost advantage of the
SRM is obvious, but the torque density is low, and shortcom-
ings of torque ripple and noise affect the application of EVs.
Next, we will introduce and compare the latest techniques that
can compensate for the shortcomings of these motors.
III. MAIN DEFECTS OF PMSM AND COMPENSATION
METHODS
A. ALTERNATIVES TO EXPENSIVE PM MATERIALS
Due to the low yield of rare earth elements and non-renewable
property, the rare earth PM is expensive. Therefore, the appli-
cation of low-cost rare earth-free PM in motors has grad-
ually attracted attention. Currently, the PM material that is
employed to manufacture motors can be classified into four
types: AlNiCo, ferrite, NdFeB, and SmCo, of which the latter
two are rare earth-containing materials [12], [13], [22].
Rare earth elements, such as Nd and Sm, can be fabri-
cated into high-energy-density PMs. However, the reserves
of these two elements are scarce and the distribution is not
concentrated in nature, which leads to high prices and an
unstable supply. Currently, the high-energy-density PM is
mainly divided into NdFeB-based PMs and SmCo-based
PMs, in which the NdFeB-based PM is the main choice for
PM motors because of their high coercivity [12]. Magnets
with low energy density and low coercivity are unable to
meet the needs of high-performance EVs, but their low cost is
very attractive to low- and medium-performance EVs. Table 2
shows the price and properties of PM materials [22].
Compared with rare earth materials, rare earth-free mate-
rials are mainly poor in coercivity and maximum energy
products, which affects the anti-demagnetization ability and
torque density, respectively, of the motor. Currently, the mag-
net market of rare earth-free magnets is mainly occupied
by AlNiCo and ferrite. The AlNiCo magnets are mainly
composed of nickel (Ni), Co, aluminum (Al), and Fe without
rare earth elements. These magnets can provide the same high
remanence as SmCo PMs but are easily demagnetized due
to their very low coercivity [12], [13], [22]. In addition,
since the Co content accounts for approximately 20% of
the total composition, the price advantage of AlNiCo is not
obvious (Table 2). However, the low coercivity contributes
an excellent field weakening ability, which can achieve a
higher speed. This high speed produces a low torque that can
generate the same power, which conserves space for the EV.
Compared with the traditional flux-weakening control mode
that requires continuous current to change the magnet rema-
nence [23], changing the magnetization level of AlNiCo-PM
requires only a simply short current pulse to be injected
into the stator [24]. Thus, the copper loss can be reduced
and the motor efficiency can be increased. The motor that
uses this variable magnetic flux characteristic is referred to
a flux memory motor [24]. In [25]–[27], this characteristic
is combined with other topology techniques to improve the
performance of the motor. For example, in [27], a new type
of memory motor which incorporates both AlNiCo-PM and
NdFeB-PM is proposed, namely the stator-PM doubly salient
flux memory (DSFM) motor. This motor can not only retain
high torque density due to NdFeB-PM but also offer efficient
air-gap flux control due to the AlNiCo-PM.
Ferrite magnets which consist of Fe oxide are the most
popular rare earth-free PMs for PM motors due to their very
low cost, although its coercivity and energy density are not
high. Currently, to compensate for the shortcomings of low
coercivity and energy density, ferrite PM motors have been
extensively investigated and are considered to be a feasi-
ble direction to solve the high cost of PM motors in the
future [13], [28]. We introduce the advanced technology of
ferrite PM motors in detail in the next part.
B. FERRITE PM MOTOR TECHNOLOGY
Due to the extremely low raw material cost, ferrite PM is
a feasible solution for reducing the cost of EV. Given the
shortcomings of relatively low energy density and coercivity,
many new techniques related to motor topology are pro-
posed, among which three kinds of topologies are employed,
namely the spoke type PM motor, axial flux PM motor, and
PM-assisted synchronous reluctance motor [29]–[36].
1) PM-ASSISTED SYNCHRONOUS RELUCTANCE MOTOR
(PMASYNRM)
The PMasynRM is similar to the IPSM whereas the difference
is the ratio of the reluctance torque and permanent mag-
net torque to the total torque. The PMasyRM improves the
reluctance torque by optimizing the design of rotor barriers,
segments, and ribs, which can partially compensate for the
decrease in torque performance caused by the reduction in
the rare earth PM. Therefore, this topology is suitable for
combining with ferrite PM as a low-cost solution with a
small sacrifice in torque performance [32], [33], [35]–[37].
FIGURE 1 and FIGURE 2 show the structure of conventional
synchronous reluctance motors without PM and PMasynRM
motors, respectively [33], [38]. Currently, the torque/PM
cost of the ferrite PMasyRM can reach 15.6 Nm/USD
whereas the cost of the mixed design of ferrite and NdFeB can
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FIGURE 1. Rotor-/stator-structure of conventional synchronous
reluctance motors without PM [38].
FIGURE 2. Structure of PMASynRM motor [33]. (a) stator and (b) rotor.
reach 17.64 Nm/USD [37], which is more than four times the
cost of the PMSM in the Toyota PRIUS 2010 [39]. However,
the low coercivity of ferrite must be considered.
To avoid demagnetization, the ferrite magnets do not fill
each layer, rather than leave space on both sides of the mag-
netic pole as flux barriers showed in FIGURE 2 to hinder the
magnetomotive force (MMF) generated by the stator. How-
ever, in the worst conditions (critical temperature is −40 ◦C,
1.5 times the maximum current), the demagnetization rate for
the ferrite magnet still exceeds 10% [35], although there are
flux barriers. To solve this problem, the authors of [35], [36]
proposed a tapered flux barrier that is designed to disperse
the flux, which reduces demagnetization. With this design,
the demagnetization rate in the worst condition is reduced
to less than 10%. FIGURE 3 shows the flux density vectors
of the second layer tapered flux barrier and common flux
barrier in the worst condition. Comparing the dotted portion
of FIGURE 3 (a) and FIGURE 3 (b), it can be seen that
the tapered magnetic barrier makes the magnetic flux more
dispersed.
2) SPOKE TYPE PM MOTOR
The spoke type PM motor can hold more volume of the
PM compared to other PM motors, and can effectively use
the reluctance torque by optimizing the rotor design [40].
Therefore, the spoke type motor is suitable for combining
with ferrite as a solution of low-cost and high-performance
PM motors [34], [41]. FIGURE 4 shows the cross-section
FIGURE 3. Flux density vectors of second-layer tapered flux barrier and
common flux barrier in the worst conditions (1.5 times the maximum
current, −40 ◦C) [35]. (a) Common flux barrier. (b) Second layer tapered
flux barrier.
FIGURE 4. Cross-section schematic of spoke type ferrite motor
design [48].
of the spoke-type ferrite motor. Currently, the spoke type
motor of ferrite PM can achieve the same torque density
as the rare earth PMSM [34]. However, considering the
demagnetization of ferrite PM and the stress of the magnet
on the rotor [42], [43], the extra space is not completely
utilized to increase the ferrite PM volume to increase the
torque density but is employed as an air barrier to enhance the
ability of anti-demagnetization. For example, in [34], due to
the large volume of the spoke type structure, air gaps can be
installed on the top and bottom of the permanent magnet as a
magnetic flux barrier, which prevents the permanent magnet
from demagnetizing in the worst conditions (1.6 times the
rated peak current, −40 ◦C). FIGURE 5 shows the field
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TABLE 3. PM motor topology technology performance comparison.
strength of a PM (white frame) with bottom and top air gaps
during a three-phase short circuit.
3) AXIAL FLUX PM MOTOR
Compared with the radial flux permanent magnet syn-
chronous motor [44]–[47], the axial flux permanent mag-
net synchronous motor has the advantages of a compact
axial structure and high torque density, so it may be a
direction to realize the low-cost PM motor. In [29], [31],
researchers proposed the high torque density axial mag-
netic flux topology with low-cost ferrite PM, and thus the
torque/PM-cost reached 9.05 Nm/USD and 13.31 Nm/USD
respectively. In [29], a ferrite PM axial gap motor with a
segmented rotor structure was proposed. This design can
make effective use of reluctance torque to compensate for
lower magnet torque, and, by increasing the number of
concentrated winding stator slots to disperse the magnetic
flux, the problem of ferrite demagnetization is alleviated.
In [31], aimed at Toyota Prius (2010)’s motor, the topology
design of the ferrite PM axial flux motor with a dual stator
is proposed.
4) FERRITE PM TOPOLOGY PERFORMANCE AND COST
COMPARISON
Table 3 compares the torque/PM cost and torque density
of the previously described PM motor design. The ferrite
PM motor that combines the advantages of the topology can
achieve the same torque density as the PMSM Toyota Prius
2010 and significantly improve the torque cost ratio. It can
be predicted that the competitiveness of rare earth-free PM
motors with a maximum torque of less than 200 Nm will
become stronger with the development of technology. How-
ever, the problem of ferrite demagnetization prevents fur-
ther improvement in torque performance. For cost-insensitive
high-performance EVs, rare earth PM combined with a
mature PMSM structure will be the main choice in the future.
FIGURE 5. Field strength (in amperes per meter) distribution during the
three-phase short circuit [34].
IV. MAIN DEFECTS OF IM AND THE CURRENT
COMPENSATION METHOD
The IM has successfully penetrated the EV market with
its mature techniques and low-cost advantages brought by
no PM (Table 1). With the development of other motor
techniques, the cost advantage of mature IM technology will
be gradually reduced. Tesla, which is the vane of the EV
industry, also began to use PM motors instead of IMs in
new products [49]. The speed range of IMs is wider and
easier to achieve than permanent magnet motors because the
magnetic field of the IM is provided entirely by electromag-
netic induction. IMs do not need to consider PM demagneti-
zation when changing the speed. However, the absence of
high-energy-density PMs cause IMs to have a low torque
performance. In addition, excitation current through the
rotor windings or bars generates loss which causes reduced
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efficiency. Therefore, the low efficiency limits the application
of IMs in EVs, which have high requirements for space uti-
lization and battery life. To compensate for this shortcoming,
many control strategies and topology technologies have been
proposed, and they will be introduced in this chapter.
A. METHODS TO IMPROVE IM EFFICIENCY
Improving the efficiency of IMs can be achieved by reduc-
ing loss and increasing the power factor. The losses can be
divided into resistance losses, core losses, and stray losses,
of which resistance losses and core losses account for the
largest proportion of total losses [50]. In terms of resistance
losses, since the operation of IMs uses the principle of electro-
magnetic induction, the resistance losses caused by the stator
excitation current and rotor induction current cannot be com-
pletely eliminated. The method of reducing resistance losses
is mainly realized by improving conductivity and conductor
current density [51], [52].
1) REDUCE MATERIAL LOSS
IM rotor conductor materials can be divided into Al, cop-
per, and alloys. Among them, copper’s electrical conductiv-
ity is nearly 60% higher than Al, which is suitable for a
high-efficiency motor. The alloy is utilized to increase the
yield strength and has no positive effect on the electrical
conductivity once it is added [53]. Considering the cost,
most rotors were composed of Al. In the past ten years,
the copper casting process was not mature enough, mainly
because no durable mold could withstand the high temper-
ature of copper casting [54], which consequently leads to
excessive production costs of copper rotors. With the devel-
opment of copper casting technology, the cost and quality of
copper rotors have met the requirements of EV mass produc-
tion [55]–[58]. In [59], the efficiency advantage of the cop-
per rotor is demonstrated and the methods for overcoming the
main difficulties in casting copper are reviewed. Moreover,
it is mentioned that a copper rotor production system with
large-scale production capacity exists. Although the copper
rotor is more expensive, its high conductivity can lead to a
reduction in the size of the motor and cost of the casing.
Therefore, there is a trade-off point between rising costs and
falling costs. To identify the trade-off point, [56] proposes a
model for obtaining a balance between size and cost, where
size is a function of efficiency.
2) SUPPRESSED FE LOSS TECHNOLOGY
The research directions for reducing the core loss of IMs
mainly include control and design. Whether to reduce Fe
loss by a design method or control strategy depends on an
accurate Fe loss calculation. Since the Fe loss is mainly
affected by the magnetic field, accurate calculation of the
Fe loss requires the distribution of the motor magnetic field.
However, the composition of the motor magnetic field which
includes the main magnetic field, harmonic magnetic field,
and leakage magnetic field, is very complicated. Therefore,
an accurate Fe loss model and simulation tools are needed to
analyze the Fe loss of the motor.
Finite element analysis (FEA) is the mainstream sim-
ulation tool. Two-dimensional 2D-FEA is the most pop-
ular method for analyzing Fe loss since it can achieve
relatively high computation accuracy and is convenient to
use [61], [62]. However, the 2D-FEA generally does not
consider the effect of the motor end shield on the Fe loss.
If the influence of the end shield is also taken into account,
the computation accuracy of the loss can be further improved.
However, this makes the complexity of the equation so much
that it needs to be calculated with 3D-FEA [63]. The dis-
advantages of 3D-FEA are a long simulation time and high
computing power requirements. Therefore, some researchers
have combined the 2D-FEA and 3D-FEA, that is, the effects
of the end shield are calculated by 3D-FEA whereas the other
parts are calculated by the 2D-FEA [64]. Moreover, some
researchers have proposed an improved 2D-FEA, which has
the advantages of 3D-FEA [65].
An accurate Fe loss model is the main basis for designing a
fast and accurate controller. Divided by type of equivalent cir-
cuit, the Fe loss model can be classified into a parallel model
and a series model [66]. The series model connects equiv-
alent resistors and magnetizing inductance in series [67].
In this model, the magnetizing inductance is assumed to be
independent of frequency, which means that the series model
has a simpler structure and is more convenient. However, this
independence leads to a certain degree of distortion of the
magnetizing inductance when the frequency changes. The
parallel model is the mainstream equivalent model, which
equates Fe loss to a set of parallel equivalent resistors and
magnetizing inductance [68]–[70]. In [68], a control method
for reducing Fe loss is proposed in the d-q coordinate system
(d-q). However, this model does not consider the influence of
stator leakage inductance, which causes a lower model accu-
racy. In [69], an improved model that takes into account the
influence of stator leakage inductance is proposed. Moreover,
some models are equivalent based on the stationary coordi-
nate system [71], [72]. Compared with the model in the d-q,
this type of model is more streamlined, however, it is difficult
to directly apply vector control because the controlled quan-
tity of the vector control is given from the d-q. Given the lim-
itations of the previous model, some researchers combined
other control techniques to study the Fe loss reduction model
in a stationary coordinate system [73]–[75]. In [75], com-
bined with direct torque control technology (DTC), a model
of Fe loss reduction based on a stationary coordinate sys-
tem was proposed. This model eliminates the complicated
calculations required for d-q transformation but inherits the
disadvantages of DTC high torque ripple. In [74], the Fe loss
reduction method combined with the high-order sliding mode
control technique is proposed without the disadvantage of
torque ripple. However, the difficulty of proving the stability
of the high-order sliding mode technique has produced very
complicated controller designs.
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In addition to the model-based method, there are also
search methods for controlling Fe loss minimization, that
is, the flux level is iteratively changed by measuring the
input power until the minimum value of the input power
is detected [76], [77]. Compared with the model-based
method, the search method is not affected by the motor
parameters, but the calculation speed is slow. Furthermore,
some scholars have proposed hybrid loss minimization con-
trol techniques that take advantage of model-based (fast) and
search (high accuracy) [78], [79]. This method calculates
the approximate optimal magnetic flux that correspond to the
minimum loss based on the model and then uses the search
algorithm to approximate the optimal magnetic flux.
3) EFFICIENCY IMPROVEMENT STRUCTURE DESIGN
Regarding the structural design of loss suppression, increas-
ing the motor shaft length is a simple method [80]. This
method only needs to increase the material of the motor
and does not need to re-manufacture the mold, but it may
reduce the torque density without redesign. Considering the
torque performance, some scholars have proposed a skew
rotor loss suppression structure [81]–[83]. This structure can
simultaneously improve efficiency and torque. In addition,
for the design of manual tuning, it is difficult to ensure that the
design is optimal and minimize other performance sacrifices.
Some scholars have proposed a multi-objective parameter
optimization method [84]–[86], which renders the motor
design faster and more balanced in performance.
4) SUMMARY OF IM EFFICIENCY IMPROVEMENT
METHODS
We summarize methods for improving the IM efficiency
in Table 4. Via topology techniques and parameter opti-
mization design, the efficiency and torque density of the IM
are the same as those of the PMSM (Toyota Prius 2010).
Combined with mature techniques, the IM has become a
low-cost motor solution for high-performance EVs. However,
with the development of PM motor techniques and increas-
ingly important endurance requirements in the future, IM’s
competitiveness in the high-performance EV market will
decrease.
V. THE MAIN DEFECTS OF SRM AND THE CURRENT
COMPENSATION METHOD
SRM has some shortcomings that hinder its application in
EVs. However, the cost of the SRM is lower than the cost
of other motors. For the condition of the same output power
of 30 kW, the material cost of SRM is approximately half of
the material cost of the PMSM (NdFeB) and less than 80% of
the material cost of the IM. If shortcomings of the SRM can
be compensated, the SRM may be able to provide low-cost
solutions for EV propulsion systems. In this chapter, we sum-
marize and compare compensation methods to mitigate the
shortcomings of the SRM.
A. INCREASE SRM TORQUE DENSITY
1) HIGH SATURATION FLUX DENSITY MATERIAL
To improve the torque density, we need to improve the mag-
netic and electric loading for a machine [87]. High saturation
flux density material can effectively improve the magnetic
loading of a machine. For example, Co-Fe type material has a
high saturation flux density that can satisfy this condition, but
it is so prohibitive that its application in an EV is not practical.
To reduce the cost of EVs, silicone steel was proposed for the
replacement of Co-Fe type material in a SRM [88]. Never-
theless, the core loss of silicone steel is much higher than that
of Co-Fe-type material, which sacrifices the efficiency of the
SRM.
2) MOTOR TOPOLOGY DEVELOPMENTS
Some structures were designed to increase the torque density
of SRMs. In [89], a new structure of the SRM, namely,
the axial-flux SRM was introduced, which enables efficient
utilization of the inner bore and coil end space. To fur-
ther increase the torque density, the relationship between
torque density and axial length (stator pole length, rotor pole
length, and rotor yoke thickness) was investigated [90], [91].
According to this relationship, three parameters (stator pole
length, rotor pole length, and rotor yoke thickness) of axial
length were separately optimized, and a maximum torque
density of 47 Nm/L was obtained, which considers the three
parameters [90], [91]. The optimization of the stator sec-
tional area could improve the torque density to 51 Nm/L
regardless of the heat dissipation.
Other strategies focused on the optimization of shapes and
numbers of the stator/rotor pole to improve the torque densi-
ties of EV motors. In [92], a design scheme that considered
the relationship between the stator/rotor pole arc (βs, βr) and
the torque density was proposed to enhance the torque density
to 30 Nm/L. However, the torque density of the SRM, which
was optimized by changing the stator/rotor pole arc, is limited
and is not comparable to that of the PM motor. In [93], engi-
neers designed a new SRM with a torque density of 45 Nm/L,
which rivaled the PM motor of that era. This design increased
the number of stator poles and expanded the stator taper
angle. The increased number of stator poles caused an
increase in the stack length of the stator and a decrease in the
coil end lengths, which elevates the magnetic loading capac-
ity. Moreover, expanding the stator taper angleis equivalent to
increasing the slot fill, which can increase the electric loading
capacity. However, this design is only suitable for medium-
and high-speed ranges. By changing the shape parameters of
the stator/rotor, researchers acquired the same torque density
(45 Nm/L) in all speed ranges [94]. These methods only
pursue high torque density without considering the influence
of other indicators that are also important for the EV, such
as torque ripple, noise, and vibration. Apart from perfecting
the mechanical parameter and construction of an EV motor,
multi-objective optimization, including torque density, was
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TABLE 4. Summary of methods for the improvement of IM efficiency.
proposed in [95], [96]. In [96], a single objective func-
tion with multi-geometry-parameters-variables was defined
as a weighted sum of the individual criteria for showing
the degree of comprehensive optimization. These geometric
parameters imply torque, torque ripple, efficiency, and torque
density. The author determined the extreme value of the
objective function, which was the trade-off point between
multiple performances. The effects of the structural optimiza-
tion and topology design are summarized in Table 5. Com-
pared with the PMSM (Toyota Prius 2010), the torque density
of the SRM has increased to the level of the PMSM (Toyota
Prius 2010).
B. REDUCE SRM AUDITORY NOISE
Auditory noise is one of the obstacles that prevents extensive
use of the SRM in an EV [97]. Vibration noise is generated
by the variation in the rotor poles radial force during the
phase commutation. Hence, the essence of reducing noise
lies in the reduction of the variation in radial force that
underlies the stator/rotor topology alternation and current
control strategy.
1) STATOR/ROTOR TOPOLOGY DEVELOPMENTS
Some methods reduce radial vibration by improving the
stator/rotor topology. In terms of the stator topology, some
researchers proposed installing rigid and nonmagnetic struc-
tural stator spacers in slot wedges to reduce vibration [98].
However, this modification reduced the winding space. The
other topology, the double stator structure, was proposed
in [99], [100], in which the rotor was assembled between the
inner stator and the outer stator, and thus, the radial force can
be counteracted. However, this structure does not reduce wind
space; instead, the manufacturing process is complicated.
In terms of rotor topology, a cylindrical rotor design, where
the salient poles were connected with thin ribs, was proposed
in [101]. It can reduce the acoustic noise at high speed.
A structure that was designed to reduce noise by skewing
the stator and rotor poles was developed in [102]. Other
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TABLE 5. Summary and comparison of torque density increase techniques.
researchers have further investigated the relationship between
skewing angles of stator/rotor pole and noise [103], [104],
and concluded that skewing the stator pole can reduce more
vibration than skewing the rotor pole.
2) CONTROL STRATEGY IMPROVEMENTS
a: INDEPENDENT CONTROL OF POLE CURRENTS
Traditional current control is a kind of three-phase current
control, in which each phase current synchronously excites
multiple stator poles. In independent pole current control,
the stator poles do not have to be simultaneously excited, and
each stator pole can be individually excited [105], [106].
Since the conduction angle of each pole can be controlled
individually, the desired radial force can be easily generated
and the control accuracy of the torque is also improved.
However, separate excitation requires a separate power switch
for each pole, which increases the cost of the power con-
verter circuit. Moreover, increasing the number of power
switches increases the number of switching losses, which
affects the efficiency, especially in the high-frequency oper-
ating state of the power switch. For instance, for high
rotation speeds, the impact on efficiency will be more
serious.
b: HYBRID EXCITATION
In [107], [108], the researchers proposed a hybrid excitation
method that combined one-phase activation and two-phase
activation. In this method, the next phase was activated before
the previous phase reached the turn-off angle, and thus, vibra-
tion can be reduced in the overlapped region.
c: RANDOMIZING TURN-on/OFF ANGLE
The randomizing turn-on/off angle method was proposed to
reduce the noise caused by resonance [110], [111]. Reso-
nance is generated when the radial force frequency coincides
with the natural frequency of the motor. In this method,
the turn-on/off angle is randomly advanced or delayed within
an appropriate limit to expand the frequency spectrum of the
radial force and reduce the possibility of resonance. However,
the determination of the random range of the turn-on/off angle
is a complex problem. If the range is too wide, the overlapping
width may be too wide to cause negative torque or too narrow
to reduce the torque generating ability [112]. However, if the
random range is too small, the radial force spectrum widening
effect is not significant. Therefore, it is necessary to conduct
in-depth research on the relationship between the random
range and the radial force spectrum to determine the optimal
random range of the turn-on/off angle to reduce the resonance
noise.
d: RADIAL FORCE HARMONICS REDUCTION STRATEGY
The radial force harmonics reduction strategy (RFH) is
another noise reduction method for resonance [113], [114].
Different from the idea of widening the radial force harmon-
ics spectrum in the random angle method, the idea of the RFH
is to derive a method for eliminating harmonics by analyzing
the harmonic model. In [114], the researchers analyzed the
relationship between harmonics and the turn-on/off angle
based on a simplified harmonic model and then proposed a
method for reducing the resonance noise according to this
relationship. It can be seen from the simulation results that the
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more advanced the turn-on angle is, the smaller the harmonic
auxiliary value is. However, the advanced turn-on angle will
cause the phase current to increase rapidly before reaching
the overlapping position because the inductance is lower at
this time. In addition, the lower inductance will produce large
phase currents but a smaller torque. Consequently, the large
phase current is wasted, which leads to a decrease in effi-
ciency.
e: TWO-STAGE COMMUTATION
A two-stage commutation strategy was proposed in
[115]–[118] to solve the vibration caused by the current
flow back when both the upper switch and the lower switch
were turned off in the nonconduction region. Compared with
the traditional switch control scheme, this strategy does not
simultaneously turn off the lower switch by turning off the
upper switch. Instead, the turn-on time of the lower switch is
appropriately prolonged to reduce the backflow current.
3) COMPARISON OF RADIAL VIBRATION MITIGATION
TECHNIQUES
Summary the Radial Vibration Mitigation Techniques:
Gan et al. summarized the radial vibration mitigation tech-
niques () that can reduce the acoustic noise. Based on their
summary, we add two methods that can reduce the resonance
noise.
C. MITIGATE SRM TORQUE RIPPLE
High torque ripple is one of the factors that hinders the SRM
application in EVs. To reduce torque ripple, many advanced
techniques that focus on the topology and current control
development have been proposed.
1) MOTOR TOPOLOGY DEVELOPMENTS
a: NUMBER OF STATOR/ROTOR POLES
In the past few years, some new designs were presented to
produce smooth torque by increasing the number of stator
and rotor poles in [120]–[122]. In [120], a novel SRM was
developed by increasing the number of rotors. However, this
design sacrifices maximum torque performance while reduc-
ing torque ripple. Therefore, the optimization needs to con-
sider other indexes. In [121], [122], other parameters, such
as the stator/rotor pole arc angle, winding connection, and
electromagnetic performance, were investigated to improve
the overall performance, including torque ripple.
b: POLE SHAPE DESIGN
In addition to the number of stator/rotor poles, the pole shape
was also investigated to reduce torque ripple [123]–[130].
A topology with a notched hole in rotor poles was inves-
tigated in [124], [128]. From this design, torque ripple
was reduced while maintaining a constant average torque.
In [126], another kind of rotor topology, in which a pole
shoe is attached to the lateral face of the rotor, was presented
to improve the average torque and simultaneously reduce
FIGURE 6. (a) Torque ripple at different turn-on angles with the CCC
control strategy when the turn-off angle is fixed at 52◦; (b) Torque ripple
at different turn-off angles with the CCC control strategy when the
turn-on angle is fixed at 37◦.
torque ripple. In terms of the stator pole optimization, a spe-
cial stator pole face shape for reducing torque ripple is pro-
posed in [125]. The author investigated the two different
slant directions of the stator: in the first case, the air gap
gradually increased during the alignment of the stator pole
and rotor pole; in the second case, the air gap was gradually
decreased in the quasi-process. It can be seen from the results
that regardless of the slanting direction, the higher the stator
pole is slanted, the larger the torque ripple is and the larger
the average torque is.
2) CONTROL STRATEGY IMPROVEMENTS
a: CURRENT AND ANGLE MODULATIONS
Angle optimization methods [131]–[133] and current pro-
filing methods [134]–[138] are traditional control strate-
gies that can reduce torque ripple. The angle optimization
method is to reduce torque ripple by selecting the appro-
priate switching angle. Generally, the turn-on angle is fixed
to adjust the turn-off angle or the turn-off angle is fixed to
adjust the turn-on angle. The effect of the different switch-
ing angles of the four-phase SRM on torque is shown in
FIGURE 6, where (a) is the motor torque at different turn-on
angles when the turn-off angle is 52 degrees with current
chopping control (CCC) and (b) is the torque at different
turn-off angles when the turn-on angle is 37 degrees with
CCC. It can be seen from FIGURE 6 (a) that when the
turn-off angle remains unchanged, the appropriate decrease
in the conduction angle can suppress torque ripple and
increase the average torque. However, if the conduction angle
decreases too much, torque ripple increases instead, because
the inductance slope is too small at this time so that the
torque/ampere is low. Nevertheless, as the inductance slope
gradually increases, the torque generated by the large current
will rapidly increase, which produces a large torque ripple.
It can be seen from FIGURE 6 (b) that a fixed turn-on angle
increases the turn-off angle to effectively suppress torque
ripple. However, the increase in the turn-off angle must be
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TABLE 6. Summary and comparison of radial vibration mitigation techniques [119].
limited to a certain range, because a large turn-off angle will
extend the tailing current to the inductance drop zone, which
produces negative torque.
The current modulation method controls the torque by
tracking the current profiling, which corresponds to the
torque from the static current characteristic. To conserve
computing power, a lookup table can be used to read the
torque value and corresponding current profiling. However,
this method requires a substantial amount of memory to store
the current profiles so that the resolution can reach the level
of identifying small torque changes. Hence, the control effect
is very dependent on the accuracy of the static characteristic
data.
b: TORQUE SHARING FUNCTION (TSF)
In traditional SRM control, torque ripple is particularly
enhanced during commutation because the previous phase
does not generate torque when it is deactivated and the
next phase does not produce adequate torque during phase
commutation [139]. To mitigate torque ripple in commu-
tation, torque sharing function (TSF) control is employed.
TSF-based control reduces torque ripple by optimizing the
phase reference torque to ensure that the sum of the phase
torques in the commutation zone is equal to the average
reference torque. The definition of the TSF function depends
on the turn-on/off angle and the overlap angle. Common
TSF types are linear, exponential, cubic, and sinusoidal.
FIGURE 7 shows the typical profiles of the linear TSF,
sinusoidal TSF and exponential TSF. FIGURE 8 shows a
FIGURE 7. Typical profiles of the linear TSF, sinusoidal TSF and
exponential TSF.
block diagram of the TSF-based torque control scheme of the
conventional three-phases SRM [140]. Each phase reference
torque (Ta_ref ,Tb_ref ,Tc_ref Ta_ref ,Tb_ref ,Tc_ref ) is allo-
cated by the TSF according to the average reference torque
(Te_ref Te) and rotor position (θθ ). Each phase reference cur-
rent (ia_ref ,ib_ref ,ic_ref ia_ref ,ib_ref ,ic_ref ) is obtained by
searching the current-position-torque characteristic table to
produce a control signal and maintain a total torque constant.
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FIGURE 8. Block diagram of TSF based torque control scheme [140].
By using TSF based control, torque ripple during com-
mutation is effectively eliminated. Some improvements have
been made on the TSF to achieve better performance and
lower torque ripple [141]–[144]. In [141], to maximize the
speed range and to reduce the copper loss, the TSF was
improved by using a genetic algorithm to optimize the turn-
on/off angle, overlap angle, and expected torque. In [142],
an optimized TSF which enabled a balance between cop-
per losses and maximize speed was introduced. However,
the TSF only works when the actual current rate of change is
greater than that of the reference. Moreover, the actual current
rate of change is inversely related to the speed, which causes
the reference determined by the TSF to be untracked and poor
torque-speed performance at high speeds. To solve this prob-
lem, in [143], the defined region of the TSF was extended
to the negative torque region to have enough time to raise or
decrease the current in the positive torque region. Another
scheme for improving tracking was proposed in [144] in
which the researcher added a compensator for the torque error
generated by the imperfect phase current track.
c: DIRECT TORQUE CONTROL (DTC)
Direct Torque Control (DTC) is a control strategy that con-
siders torque as the direct control object for the alternat-
ing current (AC) speed regulation system. DTC selects an
appropriate voltage vector based on the deviation between
the reference amplitude and the actual value of the torque and
flux linkage and then controls the converter to keep the motor
torque and flux linkage near the reference value. Because
of its reasonable dynamic response to torque changes, some
scholars have introduced DTC control into the SRM to sup-
press torque ripple [145]. Similar to AC motors, the DTC
of SRM also selects the voltage vector that is applied to the
motor based on the torque and flux linkage amplitude devi-
ation to achieve the purpose of torque control. FIGURE 9 is
a block diagram of the DTC controller of the 4-phase SRM
(ψa,ψb,ψc, and ψdare the flux linkage of each phase of
the motor; ψa, and ψbare the α-axis coordinate and β-axis
coordinate, respectively, of the synthetic flux linkage in the
α-βcoordinate system; |ψ|is the amplitude of the composite
flux linkage; δis the angle of the composite flux linkage and
the a-axis; N represents the zone where the composite flux
linkage is located; Sa,Sb,Sc, and Sdare the control signals of
each phase switch of the converter). It can be seen that DTC
does not require static characteristics to estimate parameters,
so it can be adapted to motors with the same pole number.
However, traditional DTC sector division and voltage vector
selection have obvious defects. They are based on the premise
FIGURE 9. Block diagram of DTC controller.
FIGURE 10. Single-phase switch state definition.
that the excitation current can be excited or eliminated
instantly. This defect generates negative torque and is not
conducive to a reduction in torque ripple during commutation.
In response to this defect, some new voltage vector selection
rules were proposed in [146], [147]. In [146], the vector
selection rule is further subdivided to reduce torque ripple
during commutation. In [147], a dynamic sector division rule
that changes with the speed and stator current was proposed
to demagnetize or excite in advance. This sector division rule
reduced not only torque ripple but also negative torque.
Direct instantaneous torque control (DITC) is proposed
based on DTC and average torque control (ATC) [148].
Based on DTC, DITC divides the rotation period into several
areas and formulates the switching rules of the converter
for each area. Compared with DTC, DITC has no hysteresis
comparator, and the switch state is only selected according
to the torque comparator. The design of the torque hysteresis
controller is the core of DITC, and its role is to select the
converter switching state that can reduce the torque error
according to the area of the rotor position and size of the
torque error. The definition of the switch state is shown
in FIGURE 10. When both the upper transistors and lower
transistors are on, the switch state is defined as ‘1’; and when
only the lower transistor is on, it is defined as ‘0’; when both
the upper transistors and lower transistors are off, it is defined
as ‘−1’ [145].
Some DTCs combined with other control methods have
been proposed. In [149], the instantaneous torque was con-
trolled by controlling the corresponding co-energy, which
was estimated by the current, voltage, and inductance.
In [150], a controller was designed based on the Lyapunov
function, which can handle the nonlinear characteristics of
SRM and increase the robustness of the system by mitigating
torque ripple.
d: VECTOR CONTROL
Vector control is a common control method for AC motors.
The characteristic of this method is that it can decouple the
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excitation current and torque current, and thus, achieving
flexible torque control. Some researchers have introduced
vector control to SRM control to apply its characteristics to
solve the problem of torque ripple [151], [152]. For example,
in [151], a hybrid control strategy that combines vector
control and continuous current control was proposed, which
provided a solution to the problem of torque ripple under high
speeds and a heavy load of the SRM.
Unlike traditional angular position control, vector control
avoids the need to recalculate the optimal turn-on/off angle
of each state due to the different number of rotor poles of the
SRM [151]. Because the vector control of the rotor pole pitch
is a constant 360◦electrical angle, the electrical angle of the
angular position control will vary according to the number of
SRM rotor poles.
e: MODEL PREDICTIVE CONTROL
The main feature of model predictive control (MPC) is the
use of system mathematical models to predict the future
behaviors of variables. Each prediction of MPC aims to select
the optimal switching state that minimizes the error between
the controlled variable and the reference value among all
possible converter switching states. Because MPC is intuitive
and easy to use, many scholars use MPC to reduce torque
ripple of the SRM [153]–[157].
In [155], Mikail proposed a SRM control method that is
based on the predicted phase voltage. This control strategy
can adjust the PWM duty cycle to achieve accurate current
tracking. However, this method needs to further optimize
the reference current to achieve the predetermined torque
tracking.
In [156], MPC, which predicts torque to achieve torque
ripple reduction was proposed. This method provides 30 volt-
age vectors for the controller to choose the vector that can
improve the tracking accuracy. However, a large number
of vectors leads to a long search time, which increases
the real-time difficulty. Considering this problem, in [154],
a faster torque predictive control was proposed by exclud-
ing part of the voltage vector and increasing the predictive
step width. However, in torque control, the first-order delay
between the flux linkage error and the torque error is often
disregarded, which causes a delay in torque tracking. There-
fore, some scholars designed a direct torque control strategy
with low torque ripple based on the method of predicting the
flux linkage [157].
f: SLIDING MODE CONTROL (SMC)
SMC is characterized by strong robustness [158], [159],
and its design method is not associated with parameters and
interference. Therefore, SMC is employed by many scholars
to reduce torque ripple in the SRM [160]–[165]. A speed
controller that is based on SMC to reduce torque ripple
was proposed in [160]. The controller considered the speed
error as input, and output the reference current to control
the inverter. Considering that the voltage can be used as a
control variable to more directly control the inverter, in [162],
a sliding mode flux controller with the reference voltage as
the output and combined with integral control was proposed
to reduce chatting. This method is sensitive to resistance
changes, and a small electromotive force at low speeds will
cause reduced accuracy. For this reason, Ye et al. designed
a current controller that is based on SMC, which further
improved the robustness of the system compared with the
sliding mode flux controller [165]. In addition, some scholars
integrated SMC in DTC [161], [163], [164]. For example,
in [161] a DTC speed controller with an anti-disturbance syn-
ovial observer for compensating the output reference torque
was designed based on SMC. The advantage of this kind
of method is that the switch state is directly selected to
control the torque, and the static characteristics lookup table
is not required to switch the current or flux, which conserves
storage space.
g: INTELLIGENT CONTROL
Employing classical control theory to control torque requires
knowledge of an accurate model of the SRM and solving
complex nonlinear equations. This process is cumbersome,
and compensation techniques may be needed to reduce sys-
tem errors. Therefore, the intelligent control method, which
achieves a satisfactory control effect without knowledge of an
accurate mathematical model of the system, can be regarded
as a better solution. Intelligent control includes the following
three methods: fuzzy logic control, iterative learning control,
and neural network techniques.
Fuzzy control uses language variables to describe a system,
which simplifies the complexity of the controller design.
The controller design does not depend on a complete math-
ematical model, so it is suitable for the control of non-
linear systems. Some scholars applied fuzzy control tech-
nology to reduce torque ripple in the SRM [166]–[172].
In [166], a SRM torque ripple reduction control system
was established based on an adaptive fuzzy control strategy.
FIGURE 11 shows the adaptive fuzzy controller for torque
control of the SRM. The adaptive fuzzy controller considered
the rotor position and torque error as input and output the
phase reference current for tracking. Since the controller
design does not depend on the SRM mathematical model,
it has strong robustness to the SRM with different parameters.
However, the adaptive calculation speed is slow, which yields
poor dynamic performance. In addition, the accuracy of fuzzy
control depends on the number of inputs. Increasing the
number of inputs can improve the accuracy but will expand
the search range, which increases the difficulty of real-time
calculations. Therefore, the SRM system that relies solely
on the fuzzy controller can hardly meet the requirements of
high dynamic performance and accuracy. Considering these
defects, some fuzzy control strategies combined with other
control techniques have been proposed to reduce torque rip-
ple. In [168], the fuzzy controller replaced the hysteresis
comparator in the DTC and solved the problem of the con-
trol signal distortion of the DTC when changing sectors.
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FIGURE 11. Adaptive fuzzy controller for torque control of SRM [167].
FIGURE 12. Block diagram for ILC-based current controller [173].
FIGURE 13. Details of ILC block in the current controller [173].
FIGURE 14. Diagram of B-spline neural network scheme [177].
Moreover, this design took advantage of the satisfactory
dynamic performance of the DTC.
Some scholars have proposed to design a fuzzy con-
troller to compensate for the control signal, which reduces
torque ripple caused by the nonlinearity of the SRM
model [167], [169]–[172].For example, in [170], the fuzzy
controller was employed to compensate for the current track-
ing error caused by the tail current and excitation delay in the
TSF control at high speeds. In [171], a signal compensation
strategy that is based on the neuro-fuzzy method was pro-
posed. The added neural network system can learn according
to the compensation signal output by the fuzzy controller to
adapt to different operating points.
Iterative learning control (ILC) improves the tracking per-
formance by learning the required control input from a
repetitive operation. The mechanism of learning is to store
the control input and system output error of each itera-
tion. To minimize the error, the control inputs are adjusted
TABLE 7. 8/6 SRM model parameters and control parameters.
TABLE 8. Peak-to-peak torque of each control.
according to a learning law until the error is less than the spec-
ified value. FIGURE 12 and FIGURE 13 show details of the
ILC block in the SRM controller and the block diagram for
the ILC-based current controller, respectively [173]. From
the controller, we discover that the system parameters of the
SRM do not have to be identified during operation [173].
However, during dynamic conditions, for example, the refer-
ence torque is changing, the control effect of the ILC may not
be remarkable because the previous memory is not suitable
for the tracking present reference. To solve this problem, the
P-controller is added to the loop. In addition to be applied
in the current controller, ILC is also used to compensate
for the output error in the torque-to-current-converter during
magnetic saturation [174]. In this way, the converter can
provide a more accurate current signal to the controller, and
thus, increase the torque tracking speed.
Some scholars utilized a neural network to minimize torque
ripple of the SRM [175]–[177] because controller based on
neural network did not need knowledge of the motor model
and can adjust the control system parameters by self-learning.
In [177], a B-spline neural network scheme is employed in
torque ripple reduction. By online training with the B-spline
neural network, the control system learns the relationship
between the two inputs of torque demand and rotor posi-
tion and the appropriate current profiles to reduce torque
ripple. Because the torque demand is introduced to train,
this scheme can produce acceptable dynamic performance.
FIGURE 14 shows a diagram of the B-spline neural network
scheme.
3) COMPARISON OF TORQUE RIPPLE REDUCTION
TECHNIQUES
a: SUMMARY OF THE TORQUE RIPPLE REDUCTION
TECHNIQUES
Gan et al. summarized the torque ripple reduction techniques
(Table 9) [119]. Based on their summary, we add one method
that corresponds to these techniques.
b: SIMULATION AND COMPARISON
We simulate TSF-based control, DTC, and TSF-based DITC
using MATLAB/SIMULINK and apply current chopper
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TABLE 9. Summary and comparison of torque ripple reduction techniques [119].
FIGURE 15. Block diagram of TSF-based DITC.
control (CCC) as a comparison object to study the effect
of reducing torque ripple. The control block diagram of
TSF-based DITC is shown in FIGURE 15. A four-phase
8/6 SRM with a rated power of 8 kW and a rated speed of
300 r/min was selected for simulation. The model parameters
and control parameters of the SRM are shown in Table 7. The
reference torque is set to 1 Nm.
The torque, flux linkage, and current of the four control
strategies, including CCC, TSF, TSF-based DITC, and DTC
are compared in FIGURE 17, and the torque ripple of each
control strategy is represented by the peak-to-peak torque in
Table 8. It can be seen from FIGURE 17 (a, b, c) that in
the commutation area, torque ripple increases significantly
because, at the beginning of commutation, the next phase fails
to quickly generate sufficient torque. To solve this problem,
the TSF optimizes the phase torque reference value, that
is, the sum of the reference torque of each phase at any
time is equal to the average reference torque. The reference
current that corresponds to the reference phase torque is cal-
culated according to the static characteristics and subtracted
FIGURE 16. TSF and TSF-based DITC tracking effect on reference torque
(a) is TSF control strategy, (b) is TSF-based DITC control strategy.
from the actual current to obtain the current error for out-
putting the control signal to the converter. The torque ripple
of the TSF-based torque control shown in FIGURE 17 (b) is
0.3 Nm, which uses a sine function as the torque distribu-
tion function. Compared with the torque ripple of 0.8 Nm
in FIGURE 17 (a), TSF control has an obvious suppression
effect on torque ripple.
In the TSF-based DITC, the instantaneous phase torque
directly adjusts the comparison result of the reference torque
and the instantaneous torque estimated by the static charac-
teristics according to the torque hysteresis controller. By this
strategy, the torque can be adjusted directly based on the
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FIGURE 17. Torque, current and flux diagram of 4 control strategies of 8/6 SRM. (a) CCC control
strategy, (b) TSF control strategy, (c) TSF-based DITC control strategy, and (d) DTC control strategy.
torque error rather than indirectly adjusted based on the
current error. Therefore, the tracking effect of the reference
torque is better. FIGURE 16 compares the phase torque track-
ing effect of the two control strategies (Tref is the reference
torque; Tin is the instant torque).
In the DTC control strategy, the flux linkage is main-
tained at a constant value, and torque control is achieved by
adjusting the flux linkage phase angle. In the simulation of
DTC, the reference flux linkage is 0.85 Wb, and the torque
hysteresis comparator is set to ±0.2. It can be seen from the
torque in FIGURE 17 (d) that DTC does not increase the
torque ripple caused by commutation, because DTC does not
restrict the switching angle. However, without the constraint
of the switching angle, each phase current covers almost the
entire rotation cycle, which will produce a large negative
torque and reduce the system efficiency.
Compared to the CCC strategy, the three control strategies
have effectively suppressed torque ripple. The implementa-
tion of TSF-based torque control is relatively simple, and the
performance of other aspects can be improved by optimizing
the TSF function. For example, it is proposed in [143, 144]
to reduce copper loss. Combining DITC and TSF provides
better torque ripple suppression but increases the control
complexity. DTC has a reasonable control effect on the flux
linkage and does not depend on the static characteristics
(TSF-based torque control requires i-T-θcharacteristics to
calculate the reference current, whereas TSC-based DITC
requires T-i-θcharacteristics to calculate the phase instant
torque). However, DTC will produce a large negative torque
and reduce the efficiency of the system.
VI. CONCLUSION
To solve the environmental problems caused by transporta-
tion, the popularization of EVs is very important and urgent.
Currently, short mileage and high cost are the main reasons
that hinder the popularization of EVs, and they are closely
related to electric motor technology. To solve these two prob-
lems, the motor system needs to overcome many challenges
in the future. We reviewed the challenges and compensation
methods faced by different EV motors to achieve long-range,
low-cost EVs. The prospects of each motor application in EV
are analyzed.
The rare earth PMSM has high efficiency, high torque,
the best mileage performance and the highest cost compared
to other motors with the same power. Therefore, this PMSM
is suitable for high-performance EVs that are not sensitive to
cost. The topology of the PMasyRM and spoke-type motors
with rare earth-free PM can effectively reduce the cost of
the motor and achieve higher efficiency and torque. Some
rare earth-free PM motors that target the rare earth PMSM
(Toyota Prius 2010) have been designed. It can be predicted
that among the EVs whose torque performance requirements
are similar to those of the PMSM (Toyota Prius 2010), rare
earth-free PM motors will become increasingly competitive
due to their cost advantages.
The IM is currently one of the main options for EVs. The
low efficiency rate does not renders this IM conducive to long
mileage, which is the main challenge of the IM to overcome.
The current topology technology and parameter optimiza-
tion design can improve the efficiency of the IM to that of
the PMSM (Toyota Prius 2010). Therefore, combined with
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mature technology, the IM has become a low-cost motor solu-
tion for high-performance EVs. However, with the develop-
ment of PM motor technology and the increasingly important
long mileage requirements in the future, the competitiveness
of the IM in the high-performance EV market will decrease.
The SRM has obvious advantages in the material cost.
Considering its shortcomings of low torque density, high
noise, and large torque ripple, the SRM is rarely installed in
EVs. In recent years, there have been many excellent results
in the research on improving the torque density of SRM, and
the same torque performance of the PMSM (Toyota Prius
2010) has been achieved. There are also new developments in
the techniques of reducing noise and torque ripple, including
control strategies and topology design. These techniques do
not affect the material cost of the motor and reduce the
research and development costs of EV manufacturers. There-
fore, the SRM is expected to become the solution for the EV
motor with the lowest cost and a torque density of 35 Nm/L
or less due to its low material cost.
In general, with the development of new technologies,
PM motors, IM and SRM can reach the level of PMSM
(Toyota Prius 2010) in terms of range-related performance,
but there is still a big gap with the rare earth PMSM in the
past five years. In contrast, SRM shows obvious advantages
in cost terms, but it is still difficult to apply to EVs because
of severe torque ripple and noise. In the foreseeable future,
the competition between the motor types compared in this
article that has not yet appeared will continue. There are
good reasons to believe that PMSM and IM, which have been
so successful in EV applications in the past 10 years, will
face strong competition in the next 10 years to maintain their
dominant position in passenger car production.
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ZHIKUN WANG received the B.S. degree from
Guangdong Ocean University. He is currently pur-
suing the M.Sc. degree with the Faculty of Intelli-
gent Manufacturing, Wuyi University.
TZE WOOD CHING (Senior Member, IEEE)
received the B.Eng. and M.Sc. degrees from the
Department of Electrical Engineering, Hong Kong
Polytechnic University, Hung Hom, Hong Kong,
and the Ph.D. degree from the Department of
Electrical and Electronic Engineering, The Uni-
versity of Hong Kong, Pokfulam, Hong Kong,
in 2002.
His current research interests include clean
energy, power electronic converters, electric
machines and drives, electric vehicles, and charging infrastructure. He has
more than ten years of industrial experience, working with Hong Kong
Electric Company and CLP Power (HK) Ltd., Hong Kong. He joined the
University of Macau, Macao, China, in 2004, where he is currently an
Assistant Professor with the Department of Electromechanical Engineering.
He is also an Honorary Assistant Professor with the Department of Electrical
and Electronic Engineering, The University of Hong Kong. He is a Chartered
Engineer of the U.K., a member of the Institution of Engineering and
Technology (U.K.), a member of the Chartered Institution of Building
Services Engineers (U.K.), and a member of the Hong Kong Institution of
Engineers. He has served as the Special Session Chair for IEEE-IECON12,
IEEE-ISIE13, IEEE-ICIT15, IEEE-IEMDC15, IEEE-IECON15, EVER16,
and IEEE-IEMDC2017.
SHAOJIA HUANG received the M.Sc. degree
from the University of Macau. She currently works
as a Lecturer with the Zhuhai College, Jilin Univer-
sity. Her research interests include electric vehicle
and automotive engineering.
5248 VOLUME 9, 2021

Z. Wang et al.: Challenges Faced by EV Motors and Their Solutions
HONGTAO WANG (Member, IEEE) received the
Ph.D. degree in pattern recognition and intelligent
systems from the South China University of Tech-
nology in 2015.
From January 2017 to January 2019, he was
a Visiting Research Fellow with the Centre for
Life Sciences, National University of Singapore.
He is currently a Full Professor with the Faculty
of Intelligent Manufacturing, Wuyi University, and
has been selected as a Thousand-Hundred-Ten Tal-
ent of Universities in Guangdong.