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Digital Object Identifier 10.1109/ACCESS.2017.Doi Number
Design Approaches and Control Strategies for
Energy-Efficient Electric Machines for Electric
Vehicles – A Review
Lingyun Shao1, Ahu Ece Hartavi Karci1, Davide Tavernini1, Aldo Sorniotti1, Member, IEEE,
and Ming Cheng2, Fellow, IEEE
1Centre for Automotive Engineering, University of Surrey, Guildford, GU2 7XH, UK
2School of Electrical Engineering, Southeast University, Nanjing, 210096, China
Corresponding author: Aldo Sorniotti (e-mail: a.sorniotti@surrey.ac.uk)
This work was supported by the European Union’s Horizon 2020 program under grant agreement no. 824254 (TELL project)
ABSTRACT The market penetration of electric vehicles (EVs) is going to significantly increase in the next
years and decades. However, EVs still present significant practical limitations in terms of mileage. Hence,
the automotive industry is making important research efforts towards the progressive increase of battery
energy density, reduction of battery charging time, and enhancement of electric powertrain efficiency. The
electric machine is the main power loss contributor of an electric powertrain. This literature survey reviews
the design and control methods to improve the energy efficiency of electric machines for EVs. The motor
design requirements and specifications are described in terms of power density, efficiency along driving
cycles, and cost, according to the targets set by the roadmaps of the main governmental agencies. The review
discusses the stator and rotor design parameters, winding configurations, novel materials, construction
technologies as well as control methods that are most influential on the power loss characteristics of typical
traction machines. Moreover, the paper covers: i) driving cycle based design methods of traction motors, for
energy consumption reduction in real operating conditions; and ii) novel machine topologies providing
potential efficiency benefits.
INDEX TERMS Electric machine, electric vehicle, efficiency, power loss, design parameters, control
methods, driving cycle.
I. INTRODUCTION
Socio-economic factors and technological advances are
making electric vehicles (EVs) more and more competitive
for mainstream transportation. To maintain this EV
momentum, great effort is ongoing to further develop
energy-efficient electric propulsion systems and their
primary components [1], [2], i.e., batteries, power electronic
devices, and electric machines (EMs), and to make them
commercially viable for production EVs.
In general, the operating voltage of electrified powertrains
has been increasing in recent years, because of the associated
reduction of the current levels and power losses [2], [3], and
the cost benefits in terms of connectors, cables and power
semiconductors [4]. In specific applications, e.g., in the
Toyota hybrid system [5], bi-directional converters have
been used to boost the battery voltage. This kind of high
voltage DC/DC converters can increase efficiency by
adjusting the DC-bus voltage [6]. However, new challenges
regarding insulation requirements, reliability, safety and
efficiency of components arise as extremely high voltage
systems are used in new EVs [6]-[8].
Table I illustrates the main specifications of several
representative EV models. With the largest passenger car
market, China stood for 57% of the global EV sales in the
first half of 2019 [9], e.g., see the Chinese top selling EV
models by BYD, BAIC BJEV, SAIC Motor, Geely, Chery
and JAC. The majority of production EVs has a centralized
on-board powertrain layout with one EM per axle, which is
connected to the two wheels through a mechanical
transmission with differential, half-shafts, and constant
velocity joints. For premium segment EVs, all-wheel-drive
(AWD) configurations with two EMs (one per axle) are the
latest trend, because of their intrinsically better traction and
handling performance [10]-[12]. In these configurations, the
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VOLUME XX, 2020 2
two machines can target different objectives, e.g., one
machine can be optimized for energy efficiency during
normal use, while the second one provides the required
traction torque/power performance. For example, this is the
choice of Tesla for the Model 3, which combines an
induction machine (IM) and a permanent magnet (PM)
machine [13].
TABLE I
REPRESENTATIVE PRODUCTION EVS AND THEIR MAIN EM
CHARACTERISTICS
EV models
Motor
type
Max power
(kW)
Max torque
(Nm)
Top
speed
(km/h)
Drive
type
Year
Tata Nexon
EV[24]
PM
85
200
-
FWD
2020
Porsche
Taycan Cross
Turismo[25]
-
440
900
250
AWD
2020
Honda e
advance[25]
-
113
315
145
RWD
2020
Volkswagen
id.3[25]
PM
100
275
160
RWD
2020
Kia e-Soul[25]
PM
100
395
156
FWD
2020
Volkswagen
e-golf[25]
PM
100
290
150
FWD
2019
BYD S2[27]
PM
70
180
101
FWD
2019
BJEV EX3[28]
PM
160
300
150
FWD
2019
JAC iEVS4[29]
PM
110
330
150
FWD
2019
Smart EQ
fortwo[30]
PM
60
160
130
RWD
2019
Mercedes
EQC[31]
IM+IM
300*
760*
180
AWD
2019
Kia e-Niro[32]
PM
150
395
167
FWD
2019
Roewe Marvel
X[27]
3 PMs
222*
665*
170
AWD
2018
Hyundai Kona
Electric[33]
PM
150
395
167
FWD
2018
Audi e-tron[10]
IM+IM
125+140
247+314
200
AWD
2018
Jaguar I-
PACE[11]
PM+PM
147+147
348+348
200
AWD
2018
Tesla Model
3[12]
IM+PM
147+211
639*
260
AWD
2018
Renault
ZOE[37]
SynC
80
225
135
FWD
2018
JAC iEV7L[29]
PM
50
215
110
FWD
2017
BJEV
EC180[34]
IM
30
140
100
FWD
2016
Geely
Emgrand
EV[26]
PM
120
250
140
FWD
2016
Tesla Model S
P85D[35]
IM+IM
165+350
931*
250
AWD
2016
BMW i3[36]
PM
125
250
150
RWD
2016
Chery eQ[27]
PM
41.8
150
100
FWD
2015
Fiat 500e[38]
PM
83
200
141
FWD
2013
Roewe E50[27]
PM
52
155
130
FWD
2013
Mercedes
SLS[39]
4 PMs
552*
1000*
250
AWD
2013
Nissan Leaf[22]
PM
80
280
145
FWD
2012
BYD e6 EV[27]
PM
90
450
140
FWD
2008
Tesla
Roadster[40]
IM
185
200
200
RWD
2008
Notes. AWD: all-wheel-drive; RWD: rear-wheel-drive; FWD: front-wheel-
drive; SynC: synchronous machine with rotor coils; *: combined value.
As core components of electric propulsion systems, EM
technologies have been extensively researched in terms of
motor topologies, basic characteristics, control strategies and
operating performance evaluations [14]-[18]. In the last 20
years, IMs have been the most popular EM type for EVs,
because of their low cost, high reliability, and mature
manufacturing and control techniques [14], [15]. However,
PM synchronous machines tend to have higher torque
density and efficiency than IMs, and thus are becoming
increasingly attractive for EVs [14], [16]. In particular,
interior PM (IPM) motors for passenger car applications
have higher overload capability and efficiency than IMs and
surface-mounted PM (SPM) machines [15], [17]. This
justifies the adoption of IPM machines in many EVs or
hybrid electric vehicles (HEVs) on the market, including the
Honda Accord [20], Toyota Prius [21], and Nissan Leaf [22].
The key problem of PM machines is the rapid and significant
fluctuation of the price of rare-earth materials. Therefore,
intensive research is ongoing on synchronous machines with
reduced or absent rare-earth materials [23], which has led to
the development of the PM-assisted synchronous reluctance
(PMaSynR) machines.
To increase EV mileage, energy efficiency is key in
electric powertrains. EM efficiency is influenced by many
factors, which include the machine type, topology and
geometry, control strategy, material and manufacture
technology, as well as cooling conditions. Different methods
have been proposed to improve the efficiency of EV motors.
For instance, soft magnetic lamination materials, nano-
material based conductors and high energy product PMs are
discussed in [19], to reduce iron and copper losses. However,
to the authors’ best knowledge, there are no published
articles that comprehensively summarize the design and
control methods to improve the energy efficiency of EV
machines.
To cover the gap, after discussing the typical EM
specifications for EVs in Section II, this survey provides
guidelines to improve the energy efficiency of typical
traction machines for electric passenger cars, and focuses on:
i) the main geometries, materials and construction
techniques that have direct or indirect effect on efficiency
(Section III); ii) control strategies for EM power loss
minimization at each given torque and speed (Section IV);
and iii) driving cycle based EM design methods for
minimizing the energy loss along the actual mission profile
(Section V). Moreover, Section VI presents recent advances
in EV motor topologies and designs, which have potential to
improve electric powertrain efficiency, whilst Section VII
draws the main conclusions.
The cited academic papers give a comprehensive
explanation and analysis of the main reviewed aspects,
whilst the web based references, including product brochures
and technical reports, provide solid data to show the current
state-of-the-art of EV motors.
II. ELECTRIC MACHINE SPECIFICATIONS FOR
ELECTRIC VEHICLES
Several governmental agencies have analyzed the current
and expected future trends in terms of traction motor
performance. For example, the 2018 electric machine
roadmap of the UK Advanced Propulsion Centre (APC) [41]
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VOLUME XX, 2020 3
sets the targets in Table II for passenger car traction motors,
with respect to their cost, specific power, power density and
efficiency. Also the US Department of Energy (DoE) sets
targets for EV traction machines, to be achieved by 2020 and
2025 [42]. The DoE document states that their previous
electric powertrain targets were based on 55 kW peak and 30
kW continuous power levels, under the assumption of a 325
V battery voltage and a 400 Arms maximum inverter current.
As car makers are moving forward with larger and heavier
EVs, in the 2017 release of the US DoE roadmap, the 2025
baseline peak and continuous power values were
respectively increased to 100 kW and 55 kW, with 650 V
battery voltage and 600 Arms inverter current. From 2020 to
2025, the DoE guidelines target 30% cost reduction, 89%
volume reduction, and maximum efficiency increase from
>95% to >97%. Moreover, at any speed the torque ripple
should be <5% of the peak torque [42], [43]. The
electrification roadmap of the European Road Transport
Research Advisory Council (ERTRAC) sets motor-to-wheel
efficiency target ranges for 2030, i.e., 86-91% along the new
European driving cycle (NEDC), and 87-92% along the
worldwide harmonized light vehicles test procedure (WLTP)
[45], which represent only a 1% increase from the respective
values for 2016. With the rapid development of the national
EV industry, the Chinese government also sets indicators for
next generation EV machines, e.g., peak power density >4
kW/kg and peak efficiency >96%, to be achieved by 2020
[46].
TABLE II
PERFORMANCE TARGETS FOR EV TRACTION MACHINES ACCORDING TO
THE UK APC AND US DOE ROADMAPS
Passenger car EM targets
UK APC
US DoE
2017
2025
2035
2020
2025
Inverter voltage (V) / current (A)
350/450
325/400
650/600
Cost ($/kW)
10
5.8
4.5
4.7
3.3
Continuous specific power (kW/kg)
2.5
7
9
1.6
5
Continuous power density (kW/l)
7
25
30
5.7
50
Efficiency (%)
86.51
92.51
931
>952
>972
Notes. 1: Average EM efficiency along the WLTP [44]; 2: Maximum EM
efficiency.
In general, desirable characteristics for EV traction
motors are: i) high torque capability at low speed for
acceleration and hill climb performance; ii) constant-power
speed range of 3-4 times the base speed, as a compromise
between peak torque requirement and inverter power rating;
iii) high efficiency over a wide operating range; iv)
intermittent overload capability; v) high specific power for
EV mass reduction and range extension; vi) high power
density for ease of powertrain packaging; and vii) low cost
[14]. The trade-off relations between the EV acceleration
performance, energy consumption and drive system cost
have been investigated and quantified in [47] by using
detailed load dependent loss models. As the EM torque ripple
participates in generating noise, vibration and harshness,
restricting its magnitude is also important. Table III presents
EM test results from benchmarking evaluations of typical
commercial electric powertrains, carried out for the 2005
Honda Accord [20], 2010 Toyota Prius [21], 2012 Nissan
Leaf [22], and 2016 BMW i3 [23] by the Oak Ridge National
Laboratory (ORNL), as well as available data for multiple
traction machines [48]-[58], among which the Bosch SMG
180/120 has been used in the Fiat 500e and Smart EQ fortwo.
The electric powertrain torque limits are generally
represented as functions of speed, see Figure 1, and depend
on the EM design, inverter current capability, and cooling
arrangement. The EM must be capable of uninterruptedly
operating under the continuous envelope without reaching its
thermal limits. Therefore, temperature assessments on the
hotspots at the base speed and maximum speed of the
continuous envelope are crucial during motor design [43].
Besides, a mechanical analysis should be performed to
ensure structural integrity of the rotor at the top speed. The
powertrain peak torque, generally designed for transient
overload operation, i.e., for matching the expected EV
acceleration and hill climb performance, is typically twice
the rated torque, according to [14]. The duration of the peak
torque operation is limited by the temperatures of the motor
windings, PMs and inverter, which are monitored by sensors,
see the test reports of the 2010 Prius [21] and BWM i3 [23].
The first generation of the Nissan Leaf adopted a
combination of measurement and estimation of the inverter
temperatures to decide the powertrain torque limit [22].
FIGURE 1. Motor torque-speed characteristics and qualitative overlay of
premium efficiency regions for IMs, SR motors, SPM motors and IPM
motors for EVs (extrapolation of results from [2], [15], [17], [18], [64]).
The thermal management system is essential for motor
performance, since machine efficiency and life expectancy
are governed by the magnetic losses and heat generation
[59]. As stated in [60] and [61], an appropriate cooling
system boosts the EM performance, and allows motor size
reduction, which in turn lowers vehicle weight and increases
energy efficiency. In [62] better cooling, which reduces the
coil temperature, leads to 0.25% and 0.50% increases in IM
efficiency at 100% and 125% of the nominal load. The level
of sophistication of the cooling system depends on the power
density of the machine, which is expected to significantly
increase in the next few years, according to the roadmaps in
Table II. Different cooling set-ups and design calculation
methods applicable to automotive traction motors are
reviewed in [59]. The cooling system should be designed
according to the installation conditions of the motor, while
considering cooling efficiency, reliability, manufacturing
complexity, and maintenance cost.
The torque-speed map showing EM efficiency as a
function of speed and torque is a useful tool for motor
evaluation and design [63]. However, these maps are usually
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TABLE III
EXAMPLES OF PUBLISHED DATA OF EV TRACTION MOTORS
Item
Unit
Accord
2005
Prius 2010
Leaf 2012
BMW i3
2016
Punch
Powertrain
EP2
McLaren
E-motor
Siemens
SIVETEC
MRI
Siemens
SIVETEC
MRS
Bosch
SMG
180/120
JJE OD220
JEE 90kW
Yasa P400
RS
Zytek
55kW
TM4
Motive
MV275
Protean
Pd18
Elaphe
L1500D
Reference
-
[20]
[21]
[22]
[23]
[49]
[48]
[50]
[50]
[51]
[52]
[53]
[54]
[55]
[56]
[57]
[58]
Motor type
-
IPM
IPM
IPM
IPM
SR
SPM
IM
PM
PM
PM
PM
Axial
Flux
PM
Outer
SPM
Outer
PM
Outer
PM
PM shape
-
tangent
V
delta
tangent
-
-
-
-
-
-
-
-
-
-
-
-
Cooling
-
passive
liquid
liquid
liquid
-
liquid
liquid
liquid
liquid
liquid
liquid
liquid
liquid
liquid
liquid
liquid
Stator mass
kg
12.5
16.0
-
20.2
-
26b
-
-
32b
54b
-
28.2b
62a
42b
36a
34.8b
Rotor mass
kg
10.0
6.7
16.5
14.2
-
-
-
-
Max torque
Nm
136
207
280
250
180
130
350
350
198
270
230
370
120
275
1250
1500
Max power
kW
12.4
60
80
125
>120
120
200
200
80
140
90
160
55
120
80
110
Top speed
rpm
6000
13500
10390
11400
20000
17000
20000
15000
12000
14000
12000
8000
12000
11000
1600
1516
Specific power
kW/kg
0.53
1.6
1.4
3.0
-
4.6*
-
2.6
2.5*
>4.2
-
6.7
-
2.86*
-
3.16*
Power density
kW/l
1.51
4.8
4.2
9.1
-
-
-
-
9.5*
-
-
-
-
-
-
-
Max efficiency
%
95
96
97
94
>92a
96
95
96.5
97
96
-
96
-
-
91a
94
Notes. *: Estimated data; a: Including motor and inverter; b: Assembled motor mass. Although the Honda Accord and Toyota Prius are HEVs, they have been
included given the high availability of data for their EMs. The Protean Pd18 and Elaphe L1500D are in-wheel direct drive machines.
generated from steady-state efficiency measurements or
simulations, which are incomplete for energy consumption
evaluation in applications characterized by highly dynamic
torque-speed variations. This issue is discussed in [64],
where the dynamic efficiency is computed from the
instantaneous input and output power levels at every sample
point in a driving cycle. For high performance EVs,
including many of those in Table I, the powertrain yields
high maximum torque values with respect to the typical
driving cycle requirements, and therefore generally operates
in its low torque region. For these applications, the efficiency
at very low torque demand, although rarely measured, is
essential to accurately predict EV consumption during
realistic operation.
III. EM DESIGN PARAMETERS
The efficiency characteristics are predominantly
determined by the machine type. Figure 1 overlays the
typical high efficiency regions for four EV machine types
[64], i.e., IMs, switched reluctance (SR) machines, as well as
SPM and IPM machines. For IMs, the efficiency reaches its
maximum at relatively high speed and low torque [2], and
significantly decreases from its peak because of the
important stator copper and rotor cage losses. According to
[15], IMs are “penalized by the cage losses at both low and
high speeds” compared to PM machines. According to [18],
the SR machine is associated with lower efficiency values,
and yields its maximum efficiency at higher speeds than its
PM and IM counterparts designed under the same
specifications. In comparison with IPM machines, SPM
machines are easier to manufacture and have lower copper
loss at low speeds, because of their short end turns [17].
However, the efficiency is penalized by the extra copper
losses for PM flux weakening, and the PM losses at high
speeds.
Although the shape of an EM efficiency map is
predominantly determined by the machine type, subtle
changes can be made through control modifications and
parametric design compatible with the manufacturing
constraints, to influence the resulting EM efficiency
characteristics. Some of these parameters are common
among different EM types, but many of them are motor
topology specific. The selection of the most influential
parameters is essential for fast and successful convergence
to energy efficient design. The following sub-sections
discuss leading design parameters, geometries, materials and
manufacturing techniques for the four most relevant types of
EV machines, i.e., IMs, PM synchronous machines, SR
machines and PMaSynR machines.
A. INDUCTION MACHINES
IM technologies are mature and robust; however, the
overload capability of IMs is restricted by the important heat
dissipation in the rotor, which requires appropriate air cooling
[15]. In IMs, the dominant losses are the copper losses in both
stator and rotor, which decrease at high speeds because of the
reduced magnetization current for flux weakening [18]. The
iron loss initially increases with speed, and reaches its
maximum at the base speed; then it gradually decreases in the
flux weakening region. It is desirable to have similar iron and
copper losses to maximize efficiency for each operating point
[65]. Continuous efforts have allowed to achieve significant
IM efficiency improvements through geometry design,
materials, and construction techniques, in coordination with
suitable slip control.
The design and requirements of the inverter-fed IMs used
in EVs are different from those of traditional IMs without
inverter. In fact, in conventional IMs, deep slots (Figure 2(a))
or double-cage slots (Figure 2(b)) are employed to produce a
variable rotor resistance, which is high at low speed to limit
the starting current and boost the starting torque, but low at the
rated speed for high efficiency. For inverter-fed IMs, the
desired maximum starting torque can be easily achieved by
adjusting voltage and frequency. Therefore, shallow and wide
rotor slots (Figure 2(c)), which result in low rotor resistance
and rotor leakage inductance, are suggested in [66] to keep
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VOLUME XX, 2020 5
high efficiency and power factor in a wide frequency range.
However, the negative influence of high-order harmonics
from the inverter should be considered in the design process.
In this respect, reference [65] suggests closed or half-closed
rotor slots to decrease the high-order air-gap harmonic
magnetic fields, and thus to restrain the harmonic winding
losses. Besides, it is favorable to have relatively high and
similar numbers of stator and rotor slots, with the number of
rotor slots lower than that of stator slots. In [67] closed rotor
slots with round bottoms (Figure 2(d)) were used in an
inverter-fed IM with a die-casting copper squirrel cage rotor
for a small commercial EV, achieving a maximum efficiency
of 94.4% and a wide operating region with efficiency >93%.
(a)
(b)
(c)
(d)
FIGURE 2. Typical rotor slot shapes of IMs: (a) Deep slots, (b) Double-
cage slots, (c) Shallow and wide slots, (d) Closed slots (adapted from
[66]).
Die-cast copper rotors are a proven technology to increase
IM efficiency by 1%-2% with respect to the common
aluminum rotors, by reducing the rotor ohmic losses because
of the better conductivity [68]. This solution has been
implemented in the Tesla EV motors, e.g., in the Model S.
However, these rotors pose manufacturing challenges related
to the tooling stresses and thermal shocks caused by the higher
melting point [68].
B. PM SYNCHRONOUS MACHINES
PM synchronous machines have relatively recently become
attractive to the traction motor market for EV/HEV
applications. The PM brushless machine topologies can be
categorized as SPM or IPM, respectively with magnets on the
rotor surface and inside the rotor, see Figure 3. In PM
machines, copper losses are dominant at low speeds, while
iron losses are prevalent at high speeds [63]. Furthermore, the
harmonic iron loss induced by the PM harmonic fields
becomes evident under flux weakening operation [69]. The
rotor loss is relatively small compared with the stator copper
and iron losses [14], however it is not negligible, especially in
high frequency conditions, i.e., at high speed and/or with high
pole number. The EM design process should prioritize the
dominant loss component, according to the specific operating
region of the machine.
The numbers of poles and slots have high impact on the EM
performance and dimensions. Reference [70] gives analytical
instructions for choosing suitable pole and slot combinations
for fractional-slot SPM machines to achieve low rotor losses,
i.e., it indicates 2.5 or 1.5 slots per pole for double-layer
winding configurations, and 1.5 or 1 slots per pole for single-
layer winding configurations. In both configurations, for a
given slots per pole ratio, the rotor losses continue to decrease
as the number of slots increases. In [71] a 14-pole SPM
machine achieves 13.3% lower energy consumption along the
new European driving cycle (NEDC) than its 10-pole
counterpart. Moreover, a large inductance, achieved by
increasing the number of turns and proportionally reducing the
motor axial length, improves the flux weakening capability,
and reduces the copper loss at high speeds. In [71] a design
with 14% higher number of turns and 13% shorter axial length
yields a 5% energy consumption reduction along the NEDC
than the corresponding machine with the same flux linkage
and torque production capability. However, the power factor
and inverter rating may be compromised.
(a)
(b)
FIGURE 3. Alternative PM machine topologies with magnets on the rotor:
(a) SPM, (b) IPM (adapted from [14]).
Great progress has been made with respect to the design and
manufacture of the stator windings. The concentrated winding
configuration has been employed in EMs for HEVs, such as
the Honda Accord [20] and Chevrolet Volt [72], due to the
benefits of high slot fill factor, short end-turn length and, thus,
simple winding installation, better packaging and reduced
copper loss [72]. However, this configuration brings high
magnetomotive force (MMF) harmonics, which induce
significant PM eddy-current losses and penalize efficiency at
high speeds [72]. An effective mitigation measure is the
circumferential or axial segmentation of the magnets [14]. In
[73], a shaped profile winding method is proposed to reduce
the AC loss of a concentrated-winding, open-slot PM
machine, in which the conductor profiles are shaped to run
parallel to the contours of the magnetic vector potential across
the stator slots. Another design trend is based on bar-wound
(or hairpin) flat-wire windings, which allow higher slot fill
factor, shorter end windings, and better thermal behavior than
the traditional stranded round-wire windings. Bar-wound
windings have been employed in production motors, such as
those of the 2017 Toyota Prius, Chevrolet Volt, and Roewe
Ei5. In particular, in the Chevrolet Volt, an efficiency
improvement up to 5% in the low to medium speed range is
achieved through the bar-wound construction [72]. However,
this benefit can be compromised by the skin and proximity
effects in the solid stator bars at high speeds. Therefore,
specific connection schemes are proposed in [74] to limit the
additional losses.
The PM arrangement in the rotor is essential for limiting
losses in IPM machines. The tangential-type, V-shape, and
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delta-shape PM orientations (see Figure 4) have been
employed in commercial EV motors, see Table III. As
discussed in [76], the V-shape and tangential-type
arrangements benefit from high efficiency and low torque
ripple, respectively. However, the efficiency decreases with
the increase of the width of the magnetic bridge between the
two V-shape magnets [76]. The EMs of the Nissan Leaf and
2017 Toyota Prius adopt delta-shape PM arrangements to
increase the reluctance torque and improve the high-speed flux
weakening operation [77]. In comparison with the V-shape
topology, the delta-shape design has higher torque capability
and efficiency in the constant torque region, but lower
efficiency in the flux weakening region [78]. A careful design
is required on the width of the central bridge and the shape of
the flux barriers at both ends of the magnets to restrict the
effects of the harmonics and maintain the required mechanical
strength [76], [78].
The design aspects of rotor flux barriers and their effects on
machine performance, including torque capability, torque
ripple and efficiency, are summarized in [79]. The authors of
[80] added assisted barriers to one side between adjacent poles
(see Figure 4(a)) in V-shape-rotor IPM machines, to increase
the contributions from the magnetic and reluctance torques.
Compared to the proposed design, the efficiency is increased
by 6.3%, because of the higher torque and lower iron losses.
Joint flux barriers (Figure 4(d)) are adopted in delta-shape
IPM machines to block the harmonic flux through the path
between the magnets, and reduce the PM eddy-current loss
[81]. Trapezoidal magnets and rectangular magnets with
triangular barriers are effective in reducing the harmonic iron
loss in tangential-type IPM machines [69]. In [82] a non-
uniform airgap geometry reduces the effect of the MMF
harmonics on the iron losses, which are decreased by up to
50% at high speeds, with respect to a conventional machine
with a uniform airgap.
(a)
(b)
(c)
(d)
FIGURE 4. PM arrangements in IPM machine rotors: (a) V-shape, (b)
Tangential-type, (c) Delta-shape, (d) Delta-shape with joint flux barrier
(adapted from [76], [81]).
C. SWITCHED RELUCTANCE MACHINES
SR machines are widely investigated for EVs because of
their simple and robust structure, wide constant power
capability and potentially low cost. With no PMs on the
rotor, SR machines are suitable for high-speed operation
because of the low centrifugal force on the rotor [83]. The
main restrictions are their high torque ripple and acoustic
noise, low power factor, and complex control [84]; however,
the recent literature on SR machines shows notable advances
in all these areas [85]-[87].
The most effective method to improve torque density and
efficiency is to use iron materials with high saturation flux
density and low iron loss, such as cobalt-iron-type materials,
Super Core 6.5% silicon steel, and amorphous iron [88],
[89]. In [88], an SR machine is designed to have a 96%
maximum efficiency, which is comparable to the one of the
2009 Prius IPM machine, by employing 6.5% high silicon
steel, a thinner iron sheet and a smaller airgap. Enhanced
torque/power density and efficiency are expected with higher
numbers of stator and rotor poles and smaller airgap length,
at the price of reduced constant power and overload
capabilities [84]. Besides, as pointed out in [89], the 24-16
stator-rotor pole geometry produces much lower acoustic
noise than the 6-4 or 8-6 configurations. Moreover, narrower
and longer poles bring wider constant power range and
increase high speed efficiency, while slightly sacrificing
rated torque and power [84]. However, these designs imply
tight manufacturing tolerances.
The operation of SR machines relies on precise control,
which should be compliant with the winding configuration.
Reference [87] shows that for sinusoidal current excitation,
the double layer short pitched winding has the highest
efficiency, whereas for single layer short pitched winding,
the unipolar excitation current with 180° electrical
conduction period provides the highest efficiency and lowest
torque ripple. In this respect, researches on current profile
shaping and distributed winding configurations are ongoing
to improve SR machine performance.
D. PM-ASSISTED SYNCHRONOUS RELUCTANCE
MACHINES
The PMaSynR machine derives from the synchronous
reluctance (SynR) machine, by inserting PMs into the rotor
flux barriers, see Figure 5, to improve the power factor, torque
rating and efficiency, with appropriate selection of the PM flux
magnitude [90]-[92]. This machine can also be considered an
IPM machine with high saliency and low PM usage, with the
advantages of wider speed range at constant power and
relatively lower cost. The loss components are similar to those
of IPM machines, with the exception of the PM eddy-current
loss, which is less significant because of the reduced PM
volume.
The rotor geometry plays a key role in restricting the air-
gap field harmonics, and thus the torque ripple and iron losses.
Reference [93] suggests flux barrier geometries with
combined I and U shapes (Figure 5(b)) for future PMaSynR
designs, owing to their reduced structural challenges at high
speeds, and simpler prototyping. According to [94], the flux
barrier spanning angles, defined in Figure 6, are the key
parameter to limit the stator iron losses, followed by the PM
height. The same study chooses the optimal design of the
spanning angles from the minimum loss region of the stator
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iron loss map as a function of the spanning angles, regardless
of the EM operating conditions or PM quantity. The
recommendation is for relatively high spanning angles within
the specific geometric constraints. At the optimal spanning
angles, the iron loss increment with the PM height is limited
[94]. In [95], a combined flux barrier structure, called
Machaon type, which has two wide barriers and two narrow
barriers, is applied to reduce the torque ripple by two thirds
with respect to the classic rotor design. Further considerations
can be made with respect to the thickness of the flux barriers
(see Figure 6). To limit the iron losses while maintaining the
torque production capability, the ratio of the total flux barriers
thickness, , to the rotor iron thickness, , is suggested
to be slightly higher than that of the stator slot width to the
stator slot pitch [96].
(a)
(b)
FIGURE 5. Sketches of: (a) SynR rotor, (b) PMaSynR rotor (adapted from
[95]).
FIGURE 6. Key design parameters in a PMaSynR machine rotor (adapted
from [94]).
The numbers of stator and rotor slots have high impact on
both losses and ripple. The rotor slots are realized through
the rotor teeth along the airgap surface, which are associated
with the flux barriers. In [97], the influence of the stator and
rotor slot numbers on torque ripple and iron eddy-current
loss in PMaSynR machines is evaluated through an
analytical approach based on simplified models of the stator
and rotor MMFs. The conclusion is that similar numbers of
stator and rotor slots are preferable for minimum iron loss,
while a large number of stator slots reduces the torque ripple.
IV. LOSS MINIMIZATION CONTROL METHODS
On an EV, the electric machine needs to output the
required torque to drive or brake the vehicle at the current
operating speed. Several real-time loss minimization control
(LMC) methods have been proposed to maximize EM
efficiency for given torque and speed values. These methods
can be classified into three categories: i) model based
methods, which depend on power loss models, using
analytical formulas or look-up tables, and machine
parameters; ii) adaptive search methods, based on input
power measurement, comparison, and search routines; and
iii) methods combining i) and ii).
Various LMC implementations for IM drives covering i)-
iii) are summarized in [98], including comparisons in terms
of convergence speed, dependence on EM parameters, and
accuracy. The results show that model based LMC has the
fastest convergence, but the accuracy of the solution highly
depends on the machine model and its parameters. Vice
versa, search based methods have slow convergence with
possible oscillations, but they are not affected by the system
parameters.
The selected LMC control variable, , must have
dominant influence on the EM power loss, , and its
optimal value is obtained either by solving , or
through an adaptive search routine.
Table IV summarizes the main LMC variables and power
loss models for IMs and PM machines. A simplified PM
motor power loss model considering only the copper and iron
losses caused by the fundamental current and flux is
presented in [99], which specifies the optimal d-axis current,
, for power loss minimization through an interval-reduction
search algorithm. Compared with the traditional 0
control method, the LMC in [99] improves the efficiency by
3.5% at the rated point. In [100], the optimal d-axis current
is obtained by tuning the parameters to achieve minimum
input power for each combination of torque and speed. The
power loss model in the LMC of [100] includes the stray loss,
in addition to the copper and iron losses. In [99], the iron
losses are modeled by adding an equivalent core loss
resistance, , to the traditional equivalent circuits, whereas
in [100] they are based on the empirical formula in Table IV.
However, in the above studies these LMC methods were only
tested and evaluated in steady-state conditions for given
operating points.
TABLE IV
TYPICAL POWER LOSS FORMULATIONS AND RELATED CONTROL VARIABLES
Power loss model
Ref.
Control
variable
IM
[101],
[103]
,
[104]
PM
[99]
[100]
Nomenclature. : electrical frequency; , : d- and q-axis stator currents of
IMs;, , , : d-, q-axis currents and d-, q-axis iron loss currents of PM
machines; : equivalent excitation current of PMs; , , : d-, q-axis
inductances and d-axis magnetizing inductance; : stator and rotor mutual
inductance; : PM flux; , : iron and stray loss coefficients; : saliency
ratio; = 1.5~1.6.
Although for simplicity the EM parameters are mostly
assumed constant in the model based LMC implementations,
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during real operation their variations with torque, speed and
temperature are rather significant in EV applications [101]-
[105]. The parameter variations are identified from variables
such as the load torque, temperature, stator frequency, and
voltage [101]. Real-time estimation techniques based on the
reactive power error, torque error, and an error function
based on stator voltage are proposed in [102]. The torque
error based method is employed in [104] to estimate the
stator and rotor resistances, and , for the LMC model,
in which the optimal stator flux () for minimum loss is set
through volts-per-hertz control. On the other hand, the LMC
scheme in [103] employs an online parameter adaptation
mechanism to update rotor resistance, and hence to improve
the control accuracy of the rotor magnetizing current, . A
robust linear parameter varying observer is proposed in [105]
for an IM drive controller, considering temperature
variations in both the stator and rotor resistance to estimate
speed degradation for EV operation. Reference [106]
mentions that the EM efficiency can be improved by 0.1% to
0.2% by considering the EM parameter variations with
winding and PM temperatures in the LMC model based
algorithm.
In terms of LMC results during driving cycles, in [64] the
average energy efficiency of an IM under copper loss
minimization control is increased by 3% to 6% depending on
the cycle, with respect to the rated flux control case. In [106],
the efficiency along the NEDC and WLTP achievable
through an LMC strategy is compared with that under
maximum-torque-per-ampere (MTPA) control, which
minimizes the copper loss for an IPM machine. The results
show 1% to 2% potential efficiency improvement at low-to-
medium torque and medium-to-high speed.
V. DRIVING CYCLE BASED MACHINE OPIMIZATION
Conventional EM design methodologies focus on the
rated operating point, especially for applications dominated
by steady-state behavior, e.g., pumps and fans. However, in
real-life conditions, the operating points of EV machines are
mainly far from the rated point, which may cause
discrepancy between the high efficiency areas of the torque-
speed map and the regions with high operating frequency.
Thus, the conventional EM design methods are not the most
suitable for EVs; instead, driving cycle based approaches are
highly recommended.
In this context, reference [107] deals with the
minimization of an IPM motor loss and required machine
length for a passenger car over a driving cycle, based on
finite element analysis (FEA) coupled with a population
based differential evolution algorithm. The results show that
“the longer machines with more active material tend to have
lower losses in comparison to smaller ones.” However, the
optimization process is time-consuming due to the multi-
objective, high-dimensional and nonlinear problem, with
finite element computations of the efficiency map for each
candidate design.
To accelerate the optimization routine, an option is to
select a limited number of equivalent operating points for
EM analysis and evaluation. The concepts of “geometrical
center of gravity” [108] and “energy center of gravity” [109]
have been introduced for selecting representative operating
points from a given driving cycle. In [109], the SPM motor
optimized at the rated point has lower copper energy loss but
higher iron energy loss than that the machine designed over
the representative points, and produces 17% higher motor
energy loss along the NEDC. In fact, the power loss contents
at the rated point are quite different from those at the
representative points. Therefore, the motor design at the
rated point focuses on the reduction of copper loss, whereas
the motor optimization over the representative points
achieves a balance between copper and iron losses over the
whole driving schedule. According to [108], the benefit of
driving cycle based optimization is negligible for machines
operating mostly at low speed (close to the base speed),
whereas it is significant for applications with frequent high
speed operation.
Another method to reduce the number of FEA
computations uses surrogate analytical equations or
reluctance networks to derive the power losses and voltage
at different torque and speed values. The analytical model
and reluctance network model of an SPM machine are
proposed and compared in [110], to calculate EM torque and
losses at each operating point, bringing good accuracy and
time savings in the optimization process. Reference [111]
proposes a fast method to estimate the iron losses along
driving cycles, based on the no-load and short-circuit iron
loss predicted through FEA. The FEA is performed only
once for each iteration, to calculate the base data to derive
the iron losses during the driving cycles. The combination of
energy weighted operating points and surrogate models is
adopted in [112] and [113] for the optimization of SPM and
IPM EV motors, showing fast and stable convergence. An
adaptive network based fuzzy inference system that
combines the principles of neural networks and fuzzy logic
is proposed in [114] to calculate the efficiency map for each
candidate EM design. The surrogate models in [113] and
[114] reduce the simulation time by up to two orders of
magnitude with respect to FEA.
VI. RECENT DEVELOPMENTS
Novel EM technologies for EV powertrains have been
proposed to increase operational flexibility, power density,
and system efficiency.
A. IN-WHEEL MACHINES
In-wheel motor technology brings significant potential
innovations: i) re-arrangement of the EV configuration by
placing the EMs adjacent to or inside the wheels [115], see
Figure 7, rather than on-board. The in-wheel arrangement
significantly reduces the chassis volume required by the
powertrain components and increases the space available for
the EV occupants. The motor is designed with large diameter
and short axial length to fit inside the wheel, and an outer
rotor topology is preferred for direct drive applications; ii)
continuous and seamless generation of direct yaw moments
through different wheel torque levels on the two EV sides, to
improve cornering response and active safety, by using
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VOLUME XX, 2020 9
controllers like those in [116] and [117]; and iii) enhanced
wheel torque generation accuracy, especially during extreme
transients, such as those associated with the interventions of
the anti-lock braking system (ABS) and traction control
[118]. The superior wheel torque control bandwidth of in-
wheel powertrains is caused by the absence of the torsional
dynamics of the half-shafts and constant velocity joints, and
can result in reduced stopping distances and acceleration
times [115].
Past academic studies suggest combining in-wheel
technology with PM and SR machines [119]-[121].
Currently, there are available in-wheel powertrains
developed by automotive technology firms, such as: i)
Elaphe, Protean, QS Motor, and Schaeffler, offering PM
direct drive machines (see Table III); and ii) ECOmove and
NTN, proposing near-the-wheel layouts with a PM machine
and a mechanical transmission [122], [123]. However, all of
them have yet to be extensively used in production EVs.
(a)
(b)
FIGURE 7. Examples of in-wheel motor arrangements. (a) Elaphe L1500
unit, (b) Wheel assembly with direct drive in-wheel unit (courtesy of
Elaphe).
The in-wheel powertrain efficiency is potentially
improved by the elimination of the mechanical transmission
in direct drive configurations [124], and appropriate wheel
torque distribution techniques during EV operation [125];
however, the low-speed high-torque direct drive EMs tend to
be less efficient than conventional high-speed low-torque on-
board traction motors. The literature misses a systematic
efficiency-oriented comparison of in-wheel and on-board
powertrain solutions. In this respect, preliminary results are
reported in [126]. More comprehensive analyses are being
carried out in ongoing research projects, such as the H2020
European project EVC1000 [127]. Challenges in the in-
wheel powertrain technology remain with respect to the
demanding requirements in terms of torque/power density,
safety and reliability, as well as suspension and wheel
assembly design [115].
B. NOVEL MACHINE TOPOLOGIES
In recent decades, several EM topologies have been
proposed for EVs, including stator PM, flux memory, hybrid
excitation, multiphase, magnetic geared and reconfigurable
winding machines [128].
1) Stator PM Machines. With the PMs installed in the
stator, stator PM machines inherit some of the merits of both
PM synchronous machines and SR machines, and also
overcome the PM cooling problems of rotor PM machines.
The side effects are: i) the ease of saturation of the stator iron
teeth, which limits the motor overload capability [129]; and
ii) the fact that they cannot maintain high efficiency over a
wide speed range, because of the uncontrollable PM flux.
2) Flux Memory Machines and Hybrid Excitation
Machines. To overcome the flux weakening restrictions
owing to the fixed PM excitation in PM machines, flux
memory machines and hybrid excitation machines are
proposed by applying PMs with relatively low coercive force
(such as AlNiCo magnets) and field windings, respectively,
to realize on-line flux adjustment. Since less negative d-axis
current is required, an efficiency improvement can be
expected in these types of machines, especially in the high-
speed region [130].
3) Multiphase Machines. Multiphase machines have drawn
wide attention due to their intrinsic features such as power
splitting, better fault-tolerance and lower torque ripple than
three-phase machines. Recent advances in the machine
topology, modeling and control aspects of multiphase drives
for automotive traction applications are reviewed in [131],
where the six-phase drives are extensively covered. As
discussed in [132], the motor and converter efficiencies in
three-phase and six-phase drives are very close in high
frequency applications. However, it is also indicated that an
efficiency advantage of six-phase machines can be expected
in situations in which the copper loss is much larger than the
iron loss. In multiphase EV drives, the overall converter and
motor efficiency can be enhanced by appropriate selection of
the number of active converter legs, according to the load
and speed conditions [133]. However, cost and system
reliability should be carefully evaluated, given the increased
complexity of the power electronic devices.
4) Magnetic Geared Machines. Magnetic geared machines
have been proposed to achieve low-speed high-torque
operation based on the magnetic gearing effect, which is very
desirable for direct drive applications. Different topologies
have been investigated for EVs and HEVs, such as in-wheel
magnetic geared PM machines [134], Vernier machines [135]
and magnetic geared dual-rotor machines [136]. Their
operation principle has been unified by the general field
modulation theory [137], which also provides guidance for
inventing new machine topologies. Although [134] states
that such machines have good efficiency and power density
characteristics, an objective energy efficiency comparison
with other machine topologies is currently missing, to the
best of our knowledge.
5) Reconfigurable Winding Machines. Reconfigurable
windings were originally developed to achieve faster motor
start-up in IMs [138]. However, they can also be adopted in
PM machines to expand the premium efficiency region by
switching the winding configuration from serial mode,
suitable for low-speed and high-torque operation, to parallel
mode, more efficient for high-speed and high-power
operation, see Figure 8 [23]. The switching algorithm can be
based on the efficiency values provided by each
configuration. The study in [23] evaluates the efficiency
impact of reconfigurable windings on an IPM machine
similar to the 2010 Toyota Prius motor. On this topic, recent
advances have been made on reconfiguration systems using
fewer active switches [139]. The main challenges of
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VOLUME XX, 2020 10
reconfigurable winding technology are the practical
implementation and cost of the additional switches, as well
as the complex machine geometries and assemblies [138].
FIGURE 8. Combined motor efficiency plot for serial and parallel modes
(adapted from [23]).
VII. CONCLUSION
This literature review discussed design approaches and
control methods for EV traction machines, with focus on the
efficiency enhancement over realistic EV mission profiles.
The following aspects were highlighted:
The centralized on-board powertrain layout remains the
mainstream for electric cars, and is adopted in two-wheel-
drive and all-wheel-drive EVs, where the latter provide
better traction and handling performance, particularly
desirable in premium passenger cars. An increasing
number of AWD EVs have different EM designs on the
two axles, to achieve the best compromise in terms of
efficiency, performance, and cost.
The general recent trend in EV motor topologies is toward
the adoption of PM synchronous machines with reduced
rare-earth PM material content. In this context, PMaSynR
machines represent an attractive option.
The main roadmap specifications for future EV machines
target significant power density increments and important
cost reductions. The expected efficiency increase is minor
or major, depending on the considered roadmap.
Similar and relatively large numbers of stator and rotor
slots are preferable for low harmonic losses and torque
ripple. The rotor geometries are of great importance to
limit the harmonic losses. The main relevant
characteristics are the rotor slot shapes for IMs, PM
arrangements for IPM machines, and spanning angles of
the rotor barriers for PMaSynR machines.
From the control viewpoint, LMC strategies enable the
motor to operate at its highest efficiency for each torque
and speed condition. The LMC scheme for a specific
application should be selected as a trade-off between
desired convergence speed, parameter sensitivity and
convergence error.
Driving cycle based motor design and optimization
approaches are essential to meet the energy efficiency
requirements of modern EVs. Significant work is ongoing
to reduce the computational burden of these routines,
while maintaining high accuracy of the solution.
In-wheel motor layouts offer significant benefits in terms
of vehicle design and performance, at the price of
increased complexity of the wheel hub assembly. A
systematic efficiency comparison between in-wheel and
on-board powertrain layouts is currently missing from the
available literature.
Novel EM topologies, namely stator PM, flux memory,
hybrid excitation, multiphase, magnetic geared and
reconfigurable winding machines, are potentially efficient
candidates for EV powertrains.
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LINGYUN SHAO received the B.Sc. and Ph.D.
degrees in electrical engineering from Southeast
University, Nanjing, China, in 2012 and 2018. She
is a Research Fellow in electric motor simulation
and optimization with the University of Surrey,
Guildford, U.K. Her research interests include the
design and analysis of permanent magnet
machines for electric propulsion systems and
renewable energy generation.
AHU ECE HARTAVI received the M.Sc. and
Ph.D. degrees in electrical engineering from
Istanbul Technical University, Istanbul, Turkey,
in 2000 and 2006. She is a Senior Lecturer with
the University of Surrey, Guildford, U.K. Her
research interests include intelligent control,
electric vehicle modeling and control, and
automated vehicles.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.2993235, IEEE Access
VOLUME XX, 2020 14
DAVIDE TAVERNINI received the M.Sc. degree
in mechanical engineering and Ph.D. degree in
dynamics and design of mechanical systems from
the University of Padova, Padua, Italy, in 2010
and 2014. During his Ph.D. he was part of the
motorcycle dynamics research group. He is a
Lecturer in advanced vehicle engineering with the
University of Surrey, Guildford, U.K. His
research interests include vehicle dynamics
modeling and control, mostly applied to electric
and hybrid electric vehicles.
ALDO SORNIOTTI (M’12) received the M.Sc.
degree in mechanical engineering and Ph.D.
degree in applied mechanics from the Politecnico
di Torino, Turin, Italy, in 2001 and 2005. He is a
Professor in advanced vehicle engineering with
the University of Surrey, Guildford, U.K., where
he coordinates the Centre for Automotive
Engineering. His research interests include
vehicle dynamics control and transmission
systems for electric and hybrid electric vehicles.
MING CHENG (M’01–SM’02–F’15) received
the B.Sc. and M.Sc. degrees in electrical
engineering from Southeast University, Nanjing,
China, in 1982 and 1987, and the Ph.D. degree in
electrical and electronic engineering from the
University of Hong Kong, Hong Kong, in 2001.
He is a Chair Professor with Southeast University,
Nanjing, China, where he is the Director of the
Research Center for Wind Power Generation.
Prof. Cheng is a fellow of the Institution of
Engineering and Technology. He has served as the
Chair and Organizing Committee Member for
many international conferences. He was a
Distinguished Lecturer of the IEEE Industry
Applications Society in 2015/2016.
