IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011
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Alternative Energy Vehicles Drive System:
Control, Flux and Torque Estimation,
and Efficiency Optimization
Habib-ur Rehman and Longya Xu, Fellow, IEEE
Abstract—Indirect field-oriented control, direct fieldoriented control, and direct torque control are the most
widely used instantaneous torque control techniques for the
drive system of alternative energy vehicles (AEVs). This paper
evaluates three candidate alternating-current machines and
investigates the suitability of different control techniques for the
drive systems of battery-powered electric vehicles, hybrid electric
vehicles, and integrated starter alternators. Accurate flux and
torque estimation is the core of any opted control technique for all
types of AEV drive systems. Therefore, an accurate closed-loop
voltage model flux and torque estimator that is insensitive to
stator resistance variation has been designed. This paper also
analyzes loss model control and search control (SC) techniques
for the drive system’s efficiency optimization and proposes an
offline SC efficiency optimization technique. Experimental results
are presented to demonstrate the performance of the proposed
flux and torque estimator and efficiency optimizer.
Index Terms—Direct torque control (DTC), efficiency optimization, electric vehicle, field-oriented control (FOC), flux estimation,
hybrid electric vehicles (HEVs), starter alternator, torque
estimation.
I. I NTRODUCTION
F
AST diminishing fossil fuel resources, increasing demand
for conventional personal vehicles, and concerns for environmental protection continuously promote interest in the research and development of alternative energy vehicles (AEVs)
[1]. An electric motor drive is a common propulsion system
for all kinds of AEVs. The field-oriented control (FOC), which
was invented in the early 1970s [2], and the direct torque
control (DTC), which was invented around the mid-1980s
[3], are the two most widely used techniques for almost all
types of adjustable-speed drives (ASDs), including AEVs. The
current model flux observer is mostly used for indirect fieldoriented (IFO) and direct field-oriented (DFO) control, whereas
a voltage model flux observer is chosen for DFO and DTC
types of drive systems. These observers have intensively been
Manuscript received September 7, 2010; revised April 13, 2011 and June 5,
2011; accepted June 29, 2011. Date of publication August 4, 2011; date of
current version October 20, 2011. The review of this paper was coordinated by
Dr. K. Deng.
H. Rehman is with the Department of Electrical Engineering, College
of Engineering, American University of Sharjah, Sharjah, U.A.E. (e-mail:
rhabib@aus.edu).
L. Xu is with the Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210 USA (e-mail:
longyaxu@gmail.com).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TVT.2011.2163537
examined for accurate and robust flux estimation. Accurate flux
estimation is the core of optimal flux setting and regulation.
Only accurate flux estimation can guarantee accurate torque
estimation and, thus, provide torque regulation both for the
FOC and DTC drive systems [4]–[22].
The closed-loop current model and voltage model observers
are designed for flux estimation and are documented to perform
better, compared with open-loop observers. Flux estimation,
when using current model flux observer, does not work well at
high speed due to its sensitivity to rotor resistance [4]. A closedloop voltage model flux observer is an alternative approach for
overcoming the problem of rotor resistance variation. It has,
however, been well documented [4]–[11] that flux estimation,
when using the voltage model observer, faces the problems
of stator resistance sensitivity and, most of the time, requires
additional voltage sensors. One suggested alternative [5] is
to use the current model observer at low frequency and the
voltage model observer at high frequency, thus benefiting from
their advantages and overcoming the limitations of the two
observers. Although this solution overcomes the problems of
rotor time constant and stator resistance variation, it does not
completely eliminate the observers’ reliance on the rotor and
stator resistances. Stator flux orientation [7], [8] is suggested
to reduce the effect of leakage inductance when using the
voltage model flux observer. Other stator resistance adaptation
techniques are also presented in [9]–[11], but all of these
techniques overcome the problem of stator resistance variation
by providing an estimation of the stator resistance. They neither
provide the exact value of the stator resistance nor completely
eliminate the observer’s dependence on it. In addition, accurate
voltage signal information is needed most of the time, which
requires additional voltage sensors.
Currently, sliding-mode observers are intensively investigated [12]–[22] for their effectiveness and better performance
to overcome the aforementioned problems. In our earlier work
[20], a current model observer for flux and speed estimation was
designed. The unique feature of the proposed observer was that
it was completely insensitive to the rotor time constant effect.
This paper presents a closed-loop voltage model flux and torque
estimator that does not require the stator resistance information.
In addition, voltage measurement is not required, eliminating
the need for voltage sensors. A detailed discussion on the
proposed observer design, including its stability analysis and
derivation from the generalized model of induction machine
in the stationary frame of reference, has been presented in
[21] and [22].
0018-9545/$26.00 © 2011 IEEE
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011
The FOC and DTC drive systems, which are realized in
AEVs and several other industrial applications, provide an
independent control of flux because of the natural decoupling
of the flux- and torque-producing currents. This independent
control of flux is utilized to maximize the machine efficiency
at various operating conditions. The loss model control (LMC)
and search control (SC) methods [23]–[31] are two of the
most widely used techniques for the optimal flux setting of
a vector-controlled drive system. As the name implies, LMC
needs a system model for finding the optimal flux setting at
any operating condition [23]–[27]. Therefore, this technique
is dependent on the accurate motor parameters information.
The basic principle of the SC method is to iteratively search
online for the flux settings, which require a minimum input power for a given torque and speed [26]–[31]. The SC
method suffers from the disadvantages of slow convergence
and torque ripples. This paper proposes a simple yet very
practical offline SC approach for the flux control to operate
the machine at optimal efficiency. The machine is operated
on the dynamometer in the speed control mode, and a directcurrent (dc) power generation command for battery charging
is set. The instrumentation is based on a setup that measures
the input mechanical power of the machine and the output
electrical power at the dc bus. The rotor flux (and, hence,
the flux current ids ) is iterated for minimum input mechanical
power on the motor shaft for generating the desired output dc
power. These offline optimal flux current data are collected and
used in a table lookup, which is implemented in the machine
controller for various operating conditions. The advantages of
the proposed offline SC technique, compared with the online
SC method, are listed as follows: 1) It does not suffer from
the slow convergence problem; 2) it has fewer torque ripples,
because it does not keep changing the flux level while searching
for the optimal value; and 3) it is more accurate, because
the input and output power is measured offline using voltage,
current, torque, and speed transducers, making it insensitive
to any error that could occur in the online SC technique due
to input or output power estimation. However, the disadvantage of the proposed method is that it does not account for
all the operating conditions and the machine has to intensively be characterized in the laboratory before putting it into
operation.
This paper is organized as follows. Section II compares
various candidate machines for AEV drive systems. It presents
the basic structure of IFO, DFO, and DTC techniques and investigates the suitability of these control techniques for batterypowered electric vehicle (BEV), hybrid electric vehicle (HEV),
and integrated starter alternator (ISA) applications. Section III
presents the proposed flux and torque estimator and validates
its performance through simulation and experimental study.
Finally, Section IV elaborates the proposed offline SC strategy
for the efficiency optimization, whereas the concluding remarks
are made in Section V. The work presented in this paper could
make a meaningful contribution to induction motor drives,
because the stator flux and torque observers and the efficiency
optimizer are equally useful while realizing both the FOC
and DTC drive systems used for all types of drive systems,
including AEV applications.
II. A LTERNATIVE E NERGY V EHICLE D RIVE
S YSTEM AND C ONTROL
The dc machines are no longer a viable option for AEV applications because of their low efficiency, low power-to-weight
ratio, high maintenance, and high-speed operation limitation.
The ac machines are a better alternative for the BEV, HEV, and
ISA vehicular drive systems. The induction motor, the switched
reluctance motor, and the permanent magnet motor are the three
candidate machines that are examined for these applications.
Although the switched reluctance motor has a simpler stator
winding and rugged rotor structure, it has problems of torque
ripple, noise, and vibrations. The permanent-magnet synchronous (PMS) motor, on the other hand, has higher efficiency but
is relatively more expensive, and its magnets are sensitive to
high temperature. Nevertheless, advancement in the permanent
technology and ongoing reduction in the permanent magnet’s
cost keep the future of PMS machines quite promising. The
induction motor, on the other hand, is well accepted for its wide
speed range, complete deenergization, ruggedness, and low
cost.
The 42-V ISA system has been accepted as the new standard
power net by the automotive industry. Based on the author’s
experience [32], for an ISA application, a PMS motor has a
better future compared with the ac induction motor because of
its higher efficiency, particularly when the ISA is powered by
a smaller sized battery, compared with a BEV. An ac induction
motor seems to be a better or comparable choice for BEV and
HEV applications because of the aforementioned associated
advantages. Interestingly, another deciding factor is the experience and technology that major automotive companies have
developed. Ford and General Motors are more inclined toward
the induction machine, whereas Toyota and Honda choose the
PMS machine for almost all kinds of AEV applications.
With regard to controlling the AEV drive system, FOC,
which includes IFO and DFO control, and DTC are the two
main control techniques for AEV drive systems. Figs. 1–3 show
block diagrams for IFO, DFO, and DTC drive systems, respectively. In case of speed control, the torque control loop will be
replaced with the speed control loop for FOC. For DTC, another
block for speed regulation needs to be added, whose output
will be the torque command instead of the current command,
because DTC directly regulates torque. The IFO controller,
as shown in Fig. 1, is realized by an accurate slip frequency
control—a necessary and sufficient condition for keeping the
machine field oriented. Accurate speed information is a must
for IFO drive systems, which could come from the speed sensor
or by designing a speed estimator. IFO drive systems have the
benefit of not requiring explicit stator or rotor flux estimation,
because the optimal flux setting can be realized by setting the
flux current ids and thus omitting the flux regulation block. In
our earlier work, an IFO ac induction machine drive system
was realized for ISA applications [32]. The DFO control is
preferred over the IFO control, because IFO is a feedforward
open-loop-based control system, whereas DFO is a feedback
closed-loop control system. IFO is sensitive to rotor resistance,
which is the core of slip calculation, whereas DFO is sensitive
to stator resistance, which is the core of flux and the flux angle
REHMAN AND XU: AEV SYSTEM: CONTROL, FLUX AND TORQUE ESTIMATION, AND EFFICIENCY OPTIMIZATION
Fig. 1.
IFO drive system.
Fig. 2.
DFO drive system.
Fig. 3.
DTC drive system.
estimation. In the authors’ view, both IFO and DFO are equally
good for AEV applications. The major difference is between
FOC and DTC.
DTC, shown in Fig. 3, has the features of a fast dynamic
response, the simplicity of design as a result of avoiding the
stator current, and flux regulation loops required by FOC, in
addition to robustness to the parameters’ variation. The basic
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DTC drive system also inherits a sensorless structure, and it applies a bang-bang torque and flux control. However, DTC drive
systems suffer from the problems of low-speed flux and torque
estimation and control, high current, and torque ripples. These
torque ripples create vibrations, making DTC less attractive for
automotive applications.
In the authors’ view, DTC could be a good candidate for ISA
applications, because ISA mostly operates in the cranking mode
as a motor that requires good dynamic performance. The torque
ripples of DTC drive systems in the steady-state operation are
not a major problem, because the ISA operation time as a starter
is short, and in the steady-state, it mostly operates as an alternator. For BEVs, it is only the ac motor drive that propels the
vehicle both in the dynamic- and steady-state operations. Thus,
DTC is not an attractive choice due to its torque ripples, making
FOC more suitable for BEVs. HEVs generally have the following three basic architectures: 1) series hybrid; 2) parallel hybrid;
and 3) series–parallel hybrid. The ac machine operation for
HEVs varies based on its architecture and the designed hybrid
features in the dynamic- and steady-state operations. Therefore, if the machine operation in the steady-state condition
significantly increases, FOC will be a better choice. Otherwise,
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011
DTC could be selected, but this approach would essentially
mean more of an ISA-type of operation.
Sn = [Sαs
III. F LUX AND T ORQUE E STIMATION
An accurate flux and torque estimation is the core of both
DFO- and DTC-types of drive systems. DFO and DTC may
or may not require the motor speed information, depending on
the type of flux observer incorporated. Mostly, for DFO and
DTC drive systems, a voltage model flux observer is used,
but for a DFO drive system, sometimes, a current model flux
observer is also used. An open-loop voltage model flux observer
does not require the machine speed information, whereas for an
open-loop current model observer, the speed signal information
is needed. This condition makes torque-controlled DTC and
DFO drive systems inherently sensorless when using the openloop voltage model flux observer. However, both the current
and voltage model open-loop observers are sensitive to offset
and drift problems and are, with no feedback, necessary for
convergence. Therefore, for both DFO and DTC drive systems,
closed-loop observers are preferred over open-loop observers.
In this paper, a closed-loop voltage model flux observer that is
well suited for both DFO and DTC drive systems is designed,
developed, and implemented.
The proposed closed-loop flux observer is designed based
on the estimated and measured stator currents. The current
estimation error is defined as the difference between the current
that is measured through the current sensor and the current that
is estimated through the machine model. The equations for the
proposed current and flux estimator [21] in the stationary frame
of reference are formulated as follows:
pîαs =
1
Rr
(∆αs ) −
iαs − ωr iβs
σLs
σLr
+
pîβs =
∆eq
αβs =
Sαβs = îαβs − iαβs .
(8)
where µ is the time constant of the filter. It should sufficiently
be small to preserve the slow component undistorted but large
enough to eliminate the high-frequency components. When the
trajectories of the system reach the sliding surface Sn = 0, the
observed currents îαs , îβs match the actual currents iαs , iβs .
Then, using (2) and (8), we obtain
(9)
pλ̂αs = ∆eq
αs
(1)
pλ̂βs = ∆eq
βs .
(10)
The estimated stator flux can be used to estimate the rotor flux
and flux angle as follows:
(2)
λ̂αr =
Lr
(λαs − σLs iαs )
Lm
λ̂βr =
Lr
(λβs − σLs iβs )
Lm
where
∆αβs = −u0 sign(Sαβs )

 +1, if Sαβ > 1
sign(Sαβ ) = Sαβ , if |Sαβ | ≤ 1
 −1, if S < −1
αβ
1
∆αβs
µs + 1
Thus, the stator flux can be estimated based on (2) and (9) as
pλ̂αs = vαs − Rs iαs = ∆αs
L2
σ =1 − m
Ls Lr
(7)
In this paper, subscripts α and β are used for the stationary
frame of reference, whereas notations d and q represent the
synchronous frame of reference. Equations (1) and (2) are
the current and flux estimator equations, respectively, which
include a sliding-mode function ∆αβs , as described by (4)–(6).
The sliding-mode function, which is designed around the stator
flux terms in the current estimation equation, will drive the estimated current to the measured current. When the sliding-mode
function drives the estimated current to the measured current,
the function itself gives the estimate of the stator flux, as given
by (2). However, ∆αs and ∆βs will take the extreme values of
u0 and −u0 at a high frequency and will oscillate around their
actual values. To define the control action, which maintains the
motion on the sliding manifold, an equivalent-control concept
[12] is used. Solving Sn• = 0 for ∆αs and ∆βs will yield the
equivalent-control action. In practice, the discontinuous control
can be considered a combination of an equivalent-control term
and a high-frequency switching term. Therefore, the equivalentcontrol term can be found by isolating the continuous term
using a low-pass filter, which is implemented as
∆eq
βs = vβs − Rs iβs .
1
Rr
(∆βs ) + ωr iαs −
iβs
σLs
σLr
pλ̂βs = vβs − Rs iβs = ∆βs
Sβs ]T .
∆eq
αs = vαs − Rs iαs
Rr
ωr
λ̂αs +
λ̂βs
σLs Lr
σLs
ωr
Rr
−
λ̂αs +
λ̂βs
σLs
σLs Lr
With this choice of sliding-mode functions, the sliding surface
is given by
(3)
(4)
(5)
(6)
θe = tan−1
λ̂βr
λ̂αr
.
(11)
(12)
The proposed approach thus requires the current sensors
and speed sensor or observer in the drive system, because
the currents estimation equation (1) needs the motor speed
information. In the real implementation of any induction motor
drive system, two current sensors are always in place to measure
REHMAN AND XU: AEV SYSTEM: CONTROL, FLUX AND TORQUE ESTIMATION, AND EFFICIENCY OPTIMIZATION
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TABLE I
S PECIFICATIONS OF THE L ABORATORY P ROTOTYPE I NDUCTION M OTOR
the current for the current regulation in the case of FOC and for
flux estimation and motor protection for both FOC and DTC.
DTC is inherently sensorless, but for an internal combustion
engine (ICE), a speed sensor is always in place for measuring
the engine speed, which makes the proposed observer equally
useful for both HEV and ISA applications. With regard to the
pure BEV, a sensor can be added on the machine shaft, or
because the sensorless technology is well developed, a speed
estimator can be designed to avoid an encoder on the machine
shaft. The measured current and estimated flux in the stationary
frame of reference are used to calculate the torque in a stationary frame of reference as
T̂eαβ =
3NP Lm
(λ̂αr iβs − λ̂βr iαs ).
2 Lr
(13)
Fig. 4. Simulation results for a trapezoidal-wave reference of ±10 Nm.
(a) Reference and estimated torque tracking. (b) Stator resistance variation.
(c) Actual and estimated stator current. (d) Actual and estimated rotor flux.
(e) Sliding-mode function.
The torque in a synchronous frame of reference can be
calculated by converting the measured current and estimated
flux from a stationary to a synchronous frame of reference as
T̂e =
3NP Lm
(λ̂dr iqs − λ̂qr ids ).
2 Lr
(14)
Equation (14) indicates that a given torque level can be achieved
by adjusting the current iqs , whereas the flux setting is a degree
of freedom and can be set at different values to obtain the same
level of torque. This degree of freedom is used to optimize the
machine efficiency. The proposed algorithm is validated on a
prototype 3.75-kW induction machine through the simulation
and experimental study. The parameters of the machine are
given in Table I.
Fig. 5. Simulation results for a trapezoidal-wave reference of ±10 nm,
zoomed from 0 s to 1 s. (a) Torque tracking. (b) Stator current. (c) Rotor flux
estimation.
A. Simulation Results
Fig. 4 shows the simulation results for a trapezoidal-wave
reference torque tracking. These results are zoomed between 0
and 1 s and are plotted in Fig. 5 to show a clearer performance
of the proposed algorithm. Fig. 4(a) shows the reference and
actual (estimated) torque plotted together. To test the parameter
sensitivity of the observer, the stator resistance of the machine is
changed in a magnitude of ± 50%, as shown in Fig. 4(b). When
these changes are applied, the actual machine model is used
to calculate the current, flux, and torque, whereas the proposed
observer is used to estimate the same quantities as in the actual
model. The results of the actual and estimated stator current
are plotted together in Fig. 4(c). The actual and estimated rotor
fluxes are shown in Fig. 4(d), and the sliding-mode function
(derivatives of the stator flux) is plotted in Fig. 4(e). These
results show that the estimated machine current and flux very
quickly converge to real values and are not affected by the
stator resistance variation. The accuracy of the proposed model
has quantitatively been assessed and is about 97%, showing
that the proposed model can give accurate estimation, even
when the stator resistance is not known. The estimated flux is
then used for the torque estimation and regulation presented in
Fig. 4(a). Thus, an accurate torque estimation and regulation
is performed, and the stator current and rotor flux estimation
are realized, requiring no knowledge of the stator resistance
and voltage signal information. The fact that the proposed drive
system does not require any stator resistance and voltage signal
information is the major advantage of the proposed scheme over
the schemes presented in the past [7]–[11].
B. Experimental Results for Flux and Torque Estimation
The proposed observer, after testing through a series of
intensive simulation studies, is validated in the laboratory on
an experimental test setup. The setup includes a 3.75-kW
induction motor, whose parameters are given in Table I, an
insulated-gate bipolar transistor (IGBT) inverter, and a flexible
high performance advanced controller for electric machines
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011
Fig. 6. Experimental results for ±5-nm triangular-wave torque tracking.
(a) Measured and estimated stator current. (b) Estimated stator flux.
(c) Estimated rotor flux. (d) Current estimation error.
Fig. 7. Experimental results for ±5-nm triangular-wave torque tracking.
(a) Reference and estimated torque tracking. (b) Torque command current.
(c) Rotor speed. (d) Torque error.
(ACE) [33]. The digital signal processor (DSP) on the central
processing unit (CPU) board performs all real-time control
functions, whereas a microprocessor performs downloading,
data logging, and data communication functions.
The proposed algorithm has been realized for the motor
drive that operates under rotor flux direct-field orientation, and
tests are conducted to characterize its performance. Figs. 6 and
7 show the observers’ performance for torque tracking for a
triangular-wave reference of ±5 nm, whereas Figs. 8 and 9
show torque tracking for a square-wave reference of ±5 nm.
A proportional–integral (PI) controller is used for torque regulation in both cases. These references are chosen so that the
proposed observers’ performance can be validated at sharp
changes in the torque reference and the variable and constant
torque while running in both the motoring and generating
modes of operation.
The current and flux estimation for two torque references are
plotted in Figs. 6 and 8. It can be observed through the current
Fig. 8. Experimental results for ±5-nm square-wave torque tracking.
(a) Measured and estimated stator current. (b) Estimated stator flux.
(c) Estimated rotor flux. (d) Current estimation error.
Fig. 9. Experimental results for ±5-nm square-wave torque tracking.
(a) Reference and estimated torque tracking. (b) Torque command current.
(c) Rotor speed. (d) Torque error.
[see Figs. 6(a) and 8(a)] and current error [see Figs. 6(d) and
8(d)] plots that the sliding-mode function drives the estimated
current to the measured current, and hence, the function itself
provides the estimate of stator flux according to (2) and (10).
The mean square error (MSE) between the measured and
estimated current for two cases is 0.09 and 0.10, proving that the
proposed current observer can accurately estimate the current.
An accurate current estimation guarantees an accurate estimate
of the stator flux [see Figs. 6(b) and 8(b)] and the rotor flux [see
Figs. 6(c) and 8(c)]. These estimated fluxes are finally used for
torque estimation and tracking.
The reference and estimated torques are plotted together
in Figs. 7(a) and 9(a). To realize this torque tracking, a
torque command current is generated by the PI controller [see
Figs. 7(b) and 9(b)]. The error between the command and
the estimated torque is also plotted in Figs. 7(d) and 9(d) for
triangular- and square-wave references of ±5 nm, respectively.
The MSE for the two torque tracking is 0.01 and 0.09, respectively. The difference in the MSE for torque tracking could
come from the step change that occurs for square-wave torque
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TABLE II
S PECIFICATIONS OF THE I NDUCTION M OTOR
Fig. 11. Steady-state rotor-flux-oriented model of the induction motor.
relatively high. This motor in the low-speed range, up to about
700 r/min, will mostly operate in the motoring mode. Above
the base speed in the higher speed range, it will work in the
generating mode only for ISA applications, whereas for HEVs,
it could work in both the motoring and generating modes of
operation. Therefore, the efficiency optimization is mainly required for motor operations above the speed of about 700 r/min.
Below this speed, the machine could be operated at the rated
flux.
B. Drive System Efficiency Optimization
Fig. 10. Torque speed characteristics of the HEV/ISA machine.
tracking, or it could be due to the tuning of the PI controller’s
gains. However, the MSE for the current estimation for the two
cases is about the same, validating the observer performance.
The speed signal is also included for completeness and readers’
information [see Figs. 7(c) and 9(c)]. The proposed observer
proved to effectively regulate the torque while estimating the
stator and rotor fluxes of the machine.
IV. M OTOR D ESIGN C ONSIDERATIONS
AND E FFICIENCY O PTIMIZATION
This paper takes the HEV/ISA drive system as a case study
to demonstrate the proposed offline SC technique for efficiency
optimization. The motor design for HEV/ISA is relatively different, compared with other variable-speed drive applications.
Therefore, first, the HEV/ISA motor design considerations are
discussed, and then, the efficiency optimization is presented in
this section.
A. Motor Design Considerations
The HEV/ISA machine, most of the time, operates in the
generation mode and needs to supply a constant voltage over
a wide range of speed for battery charging. This requirement
minimizes the constant torque region and extends the constant
power region over a ration of 1–6. The motor rating for a typical
prototype HEV/ISA application is shown in Table II, and the
desired torque speed characteristics are shown in Fig. 10.
Typically, the motor efficiency is quite low for small-size motor
drives of less than 10 kW, whereas the converter efficiency is
The induction motor operates at different torque and speed,
depending on the vehicle operating conditions. An indirect
rotor-flux-oriented drive system is realized in this paper. The
steady-state model of this drive system is shown in Fig. 11. The
motor steady-state stator copper losses, rotor copper losses, and
core losses are, respectively, given by [27]
Ps = Rs i2ds + i2qs
2
ωs Lm
ids
Pr = Rr iqs −
Rf e
1
Pf e = (ωs Lm )2
i2 .
(15)
Rf e ds
For any given speed and load torque, a flux level exists, at
which the copper and core losses will be the same. This flux
level will maximize the motor efficiency. Thus, the problem of
energy optimization nails down to finding and then controlling
the motor at this optimal flux level. LMC and SC are two of
the most widely used techniques for the optimal flux control.
In addition, a hybrid technique has been developed, which uses
a motor model to search for the minimum power loss and then
applies the SC method for further optimization. LMC uses the
motor model to achieve minimum losses. Various minimization
variables are suggested in the literature for the model-based
technique. One of these methods is to transform the stator
copper losses, rotor copper losses, and core losses given by (15)
into the following d and q components:
2 1
2 Rr
i2
+ Rs + (ωs Lm ) 2
Ploss_d = (ωs Lm )
Rf e
Rf e ds
Ploss_q = (Rr + Rs )i2qs .
(16)
The optimal flux level can be achieved by equating the losses
depending on the current direct with the rotor flux equal to
the losses depending on the current in quadrature to the rotor
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 8, OCTOBER 2011
flux [27]. The model-based method clearly requires an accurate
knowledge of the motor parameters for achieving the optimal
flux level. This dependence on the motor parameters could shift
the machine operation from the optimal flux level.
The SC method works on the principle of iteratively changing the flux level and searching for the minimum input power
while keeping the output speed and load constant, i.e., operating
in the steady state at a constant output power. The input variable
can be a magnetizing flux, stator voltage, or direct-axis stator
current. The optimization of any of these variables will minimize the input power. Perturb and observer, fuzzy logic, and
neurofuzzy have been used in the literature for the SC method.
The advantage of the online SC method is that it does not
need the motor parameters that are required by the model-based
approach. However, this techniques suffers from the problems
of slow convergence and torque ripples and needs precise power
measurement. Therefore, this paper proposes a simple offline
SC method for the optimal flux control of the drive system.
The rotor-flux-oriented drive system that is implemented in
this paper has torque and flux commands as two independent
control inputs, which can be adjusted to achieve the same level
of output power. The torque command is mostly set by the
vehicle operating conditions, whereas the flux command is a
degree of freedom that can be adjusted at different levels while
achieving the desired speed and torque. This freedom in the
adjustment of the flux level is used to maximize the system
efficiency. The machine is operated by the dynamometer in
the speed control mode, and a dc power generation command
for the battery charging is set. The rotor flux (and, hence, the
flux current ids ) is iterated for the minimum input mechanical
power on the motor shaft to generate the desired dc output
power. The instrumentation is based on a setup to measure
the input mechanical power of the machine and the output
electrical power at the dc bus. The procedure is repeated for
different levels of dc power commands at different speed. These
offline optimal flux current data are collected and used in a
table lookup, which is implemented in the machine controller
for various operating conditions.
Fig. 12 shows the efficiency optimization at 1000 r/min. The
command dc power for battery charging is varied from 250 W
to 3 kW, and ids is iterated for efficiency optimization.
Fig. 12(a)–(c) shows the optimized values of ids , the input
mechanical torque, and the shaft input power to achieve the
desired dc output electrical power, respectively. Finally, the
efficiency of the drive system is plotted in Fig. 12(d).
The efficiency is recorded from the motor shaft to the dc bus,
which also includes the inverter efficiency. However, it still
realizes the optimal flux level for the efficiency maximization
of the machine. In Fig. 13, the flux optimization is demonstrated at speeds of 2000, 3000, 4000, and 4500 r/min. The
optimum flux current and efficiency are plotted in each trace
for each speed. Fig. 14 shows the combined efficiency plots for
the aforementioned five different speeds for a desired output
electrical power ranging from 250 W to 3 kW. It is shown that
the machine efficiency is significantly low when the machine
operates with light load and low speed. This difference could be
much more significant if the flux optimization is not performed
and if the machine is operated all the time at a constant/rated
Fig. 12.
Experimental results for efficiency optimization at 1000 r/min.
Fig. 13.
Experimental results for efficiency and flux current.
flux. The data plotted in these figures are used in the motor
controller of HEV/ISA, in which, whenever the torque command changes, the controller will select an appropriate flux
level for the efficiency optimization. Although this proposed
offline SC efficiency optimization technique requires a rigorous
characterization of the machine on the dynamometer, it does
not suffer from the slow-convergence problem, has fewer torque
ripples compared with the online SC method, and is insensitive
to parameters variation, compared with the LMC technique.
V. C ONCLUSION
This paper has evaluated the suitability of torque control
techniques for different types of vehicular drive systems and designed a flux and torque estimator and an efficiency optimizer.
Major conclusions that can be drawn from this paper are listed
as follows.
• FOC and DTC are the two main types of control techniques. The DTC drive system is more suitable for ISA
applications. For BEV applications, FOC is a preferable
choice over DTC because of torque ripples that produced
by DTC. For HEV drive systems, the choice between DTC
and FOC depends on the motoring features of ac machine
operation in the steady-state condition.
REHMAN AND XU: AEV SYSTEM: CONTROL, FLUX AND TORQUE ESTIMATION, AND EFFICIENCY OPTIMIZATION
Fig. 14. Experimental results for efficiency optimization at various speeds.
• The flux and torque estimators presented in this paper are
well suited for vehicular drive system applications. For
DTC drive systems, the proposed estimator loses DTC’s
inherent sensorless nature, but mostly, a speed sensor is
already installed in conventional personal-use vehicles.
• A simple offline SC-based technique for efficiency optimization is proposed. The proposed SC technique does
not suffer from the slow-convergence problem, has fewer
torque ripples, and is more accurate because of the actual
measurements taken through instrumentation in the laboratory. However, the disadvantage of proposed method
is that it may not account for all the operating conditions
and requires an intensive characterization in the laboratory
before putting it into the vehicle.
• Future works include an intelligent online SC controller
design for the efficiency optimization using the efficiency
data collected in the laboratory. The torque estimator that
is proposed in this paper has strong potential for accurate
power calculation required online.
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Habib-ur Rehman received the B.Sc. degree in
electrical engineering from the University of Engineering and Technology, Lahore, Pakistan, in 1990
and the M.S. and Ph.D. degrees in electrical engineering from the Ohio State University, Columbus,
in 1995 and 2001, respectively.
He has wide experience in power electronics and
motor drives in both industry and academia. From
July 1998 to December 1999, he was a Design
Engineer with the Ecostar Electric Drives and Ford
Research Laboratory, where he was a Member of
the Electric, Hybrid, and Fuel-Cell Vehicles Development Programs. From
2001 to 2006, he was with the Department of Electrical Engineering, United
Arab Emirates University, Al-Ain, U.A.E., as an Assistant Professor. In 2006,
he joined the Department of Electrical Engineering, College of Engineering,
American University of Sharjah, Sharjah, U.A.E., where he is currently an
Associate Professor. His research interests include microprocessor/digitalsignal-processor-based adjustable-speed drives, power electronics, alternative
energy vehicles, and renewable energy systems. Currently, he is investigating
the effective delivery of design skills in engineering education, particularly in
electrical engineering.
Dr. Rehman is the recipient of the Best Teacher Award from the College of
Engineering, UAE University, for the academic year 2002–2003.
Longya Xu (S’89–M’90–SM’93–F’04) received the
M.S. and Ph.D. degrees in electrical engineering
from the University of Wisconsin, Madison, in 1986
and 1990.
In 1990, he joined the Department of Electrical
and Computer Engineering, the Ohio State University (OSU), Columbus, where he is currently a
Professor. He has served as a Consultant to several industrial companies, including the Raytheon
Company, Boeing, Honeywell, GE Aviation, the U.S.
Wind Power Company, General Motors, Ford, and
Unique Mobility Inc., for various industrial concerns. He is currently the
Director of the newly established Center of High Performance Power Electronics, OSU, which is supported by the Ohio Third Frontier Program. His
research and teaching interests include the dynamics and optimized design of
special electrical machines and power converters for variable-speed systems,
the application of advanced control theory and digital signal processors for
motion control, and distributed power systems in super high-speed operations.
Over the past 20 years, he has conducted several research projects on electrical
and hybrid electrical vehicles and variable-speed constant-frequency wind
power generation systems.
Dr. Xu is currently a Member-at-Large for the IEEE Industry Applications
Society (IAS) Executive Board. He has served as the Chair of the Electric
Machine Committee of the IEEE IAS and an Associate Editor for the IEEE
TRANSACTIONS ON POWER ELECTRONICS. He received the First Prize Paper
Award from the Industry Drive Committee of the IEEE IAS in 1990, the
Research Initiation Award from the National Science Foundation in 1991 for
his work on wind power generation, and the Lumley Research Award from the
College of Engineering, OSU, in 1995, 1999, and 2004, for his outstanding
research accomplishments.