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Efficient Antilock Braking Systems
in Regenerative Brakes
Submitted in total fulfilment of the requirements of the
degree of Doctor of Philosophy
Amir Dadashnialehi
Faculty of Engineering and Industrial Sciences,
Swinburne University of Technology Melbourne, Australia, 2014
i
Abstract
The design of electric vehicles (EVs) is increasingly based on In-Wheel
technology by which a separate electric motor at each corner of the
vehicle provides the opportunity to design innovative and unique vehicle
subsystems. This thesis aims to explore the feasibility of the development
of efficient antilock braking systems (ABSs) for In-Wheel EVs.
The main idea is to design an intelligent sensorless ABS for In-Wheel EVs.
The proposed design eliminates the need to install separate conventional
ABS sensors, significantly reducing the vehicle's manufacturing and
maintenance cost. The design also improves the performance of the ABS
by accurate wheel speed estimation and road identification, using wavelet
signal processing.
The proposed sensorless system was developed for both brushed and
brushless In-Wheel EVs, and extensively tested using actual ABS hardware
and motors. The experimental results showed that
(a) the proposed sensorless wheel speed estimation was more accurate
than that of the commercial ABS sensors
(b) sensorless ABS for brushless propulsion had a higher accuracy
compared with that of brushed propulsion.
An alternative proposal in this thesis is to develop an efficient ABS by
investigating the feasibility of a Sensor-fusion-based ABS for In-Wheel EVs.
In this approach, the data fusion concept was used to improve accuracy,
ii
reliability and robustness for the wheel speed measurement system of the
ABS. The proposed Sensor-fusion-based ABS was realised for both brushed
and brushless In-Wheel EVs, and extensively evaluated using actual ABS
hardware and motors.
The experiments showed that the wheel speed estimation, based on the
proposed data fusion method for brushed In-Wheel EVs, was significantly
more accurate than conventional vehicles. Our experiments also showed
that accuracy of the wheel speed estimation of the brushless propulsion
was also higher than both commercial ABS sensor and brushed
propulsions. The robustness of the proposed design was also investigated
and shown to be superior to the ABS for conventional vehicles.
iii
Acknowledgment
I sincerely thank my supervisors, Zhenwei Cao, Ajay Kapoor, and Alireza
Bab-Hadiashar, for their help, supports and insightful comments during
my PhD studies. This project would not have been possible without them.
I acknowledge the financial support of the AutoCRC, and thank the
AutoCRC staff, especially Garry White and Jacqueline King, for their
assistance during this project.
I also thank Swinburne university staff members Mellissa Cogdon, Krys
Stachowicz, Warren Gooch, Walter Chetcoti, Michael Mayrow, Ralph
Timpano, and Swinburne workshop staff (Alec, Meredith, and Peter) for
their help.
Finally, I thank my family, especially my mother, for their love, support
and encouragement.
iv
Declaration
This is to certify that:
1. This thesis contains no material which has been accepted for the
award to the candidate of any other degree or diploma, except
where due reference is made in the text of the examinable
outcome.
2. To the best of the candidate’s knowledge, this thesis contains no
material previously published or written by another person except
where due reference is made in the text of the examinable
outcome.
3. The work is based on the joint research and publications; the
relative contributions of the respective authors are disclosed.
4. This thesis has been professionally copy-edited by Christina Crossley
Ratcliffe AE, of Quillpower, Melbourne, according to the guidelines
laid out in the university-endorsed national 'Guidelines for editing
research theses.'
______________________
Amir Dadashnialehi, 2014
v
Contents
I. Introduction ...........................................................................................................................2
II. Literature Review ...................................................................................................................6
A. Introduction to ABS ............................................................................................................6
1) ABS sensor ......................................................................................................................8
2) Intelligent ABS ............................................................................................................. 14
B. Introduction to Wavelets ................................................................................................ 18
1) Continuous Wavelet Transform (CWT) ....................................................................... 18
2) Discrete Wavelet Transform (DWT) ............................................................................ 19
3) Wavelet Packet (WP) .................................................................................................. 21
C. Introduction to Data Fusion ............................................................................................ 23
1) Ordered Weighted Averaging (OWA) ......................................................................... 25
D. Summary of Chapter II .................................................................................................... 28
III. Experimental System ...................................................................................................... 30
A. Standard ABS Test Rig ..................................................................................................... 30
B. ABS Test Rig Modifications ............................................................................................. 33
C. Computer Simulations .................................................................................................... 36
D. Summary of Chapter III ................................................................................................... 41
IV. Intelligent Sensorless ABS ............................................................................................... 43
A. Intelligent Sensorless ABS for Brushed In-Wheel EVs ..................................................... 46
1) Experimental Results and Discussions ........................................................................ 51
B. Intelligent Sensorless ABS for Brushless In-Wheel EVs ................................................... 73
1) Experimental Results and Discussions ........................................................................ 82
C. Summary of Chapter IV ................................................................................................... 94
V. Sensor-fusion-based ABS .................................................................................................... 96
A. Sensor-fusion-based ABS for Brushed In-Wheel EVs ...................................................... 99
1) Experimental Results and Discussions ........................................................................ 99
B. Sensor-fusion-based ABS for Brushless In-Wheel EVs .................................................. 106
1) Experimental Results and Discussions ...................................................................... 108
C. Summary of Chapter V .................................................................................................. 115
VI. Conclusion and Future Work ........................................................................................ 117
vi
List of Figures Figure II-1. The role of ABS. ............................................................................................................7
Figure II-2. A commercial ABS sensor (made by Repco) used in sedan cars. .................................9
Figure II-3. A single tooth of the ABS ring in the proximity of the winding. ..................................9
Figure II-4. Typical output voltage of a commercial ABS sensor (made by Repco Ltd). ............. 11
Figure II-5. Result of converting output of the ABS sensor (shown in Figure II-4 ) to frequency.
.................................................................................................................................................... 12
Figure II-6. Corrected speed signal from the ABS sensor output. ............................................... 13
Figure II-7. Effect of noise (slip measurement errors) on the stopping distance of a vehicle with
ABS (with On-Off controller). ...................................................................................................... 17
Figure II-8. Implementation structure of the Discrete Wavelet Transform (DWT). ................... 20
Figure II-9. Mother wavelet functions of Haar, db2 and dmey wavelets. .................................. 20
Figure II-10. Implementation structure of DWT and WP; while WP decomposes both the higher
(Details) and the lower frequency (Approximation) components at each level of
decomposition, the DWT proceeds to decompose only the lower frequency bands. .............. 21
Figure II-11. Schematic diagram of redundant and complementary information...................... 24
Figure III-1. ABS experimental setup [53] for conventional brakes (picture available at
www.inteco.com.pl). ................................................................................................................... 30
Figure III-2. ABS test rig and data acquisition systems. .............................................................. 32
Figure III-3. Main steps of adding a commercial ABS sensor to the ABS test rig. The wheel hub
with an integrated ABS sensor is shown in the bottom part. The disassembled ABS sensor is
shown in the middle, and the coupling of the ABS ring to the test rig is shown in the top part.33
Figure III-4. Installation details of a DC motor and conventional ABS sensor on the standard ABS
test rig. ........................................................................................................................................ 35
Figure III-5. Schematic diagram of the experimental ABS test rig for conventional brakes. ...... 36
Figure III-6. Friction coefficient vs. wheel slip for asphalt and icy road conditions. ................... 39
Figure III-7. A typical simulation model in MATLAB Simulink Software...................................... 40
Figure IV-1. Role of sensorless ABS in saving physical space in the In-Wheel hub. .................... 44
Figure IV-2. General architecture of a braking system of an EV with four independently driven,
DC-motor in-wheel hubs. ............................................................................................................ 50
Figure IV-3. Modified version of the simulation file shown in Figure III-7.................................. 52
Figure IV-4. Results of decomposing the armature voltage (in volts) by Haar wavelet, (A) dry
asphalt (B) road surface changes from dry asphalt to icy road 15 m after the start of the
braking phase. ............................................................................................................................. 54
Figure IV-5. (A) The additive noise signal introduced to the system (B) Effect of noise on the
back EMF signal. .......................................................................................................................... 56
Figure IV-6. Results of decomposing the experimental armature voltage by Haar wavelet. Top
figure: Dry asphalt. Bottom figure: Road surface changes from dry asphalt to oily road around
the speed of 600 r/min. .............................................................................................................. 58
Figure IV-7. Comparison between the back EMF signal and the encoder signal for dry road and
initial speed of 2300 rpm. ........................................................................................................... 59
Figure IV-8. Comparison between the back EMF signal and the encoder signal (during ABS
activation) for dry road and initial speed of 1000 rpm. .............................................................. 60
vii
Figure IV-9. Comparison between the back EMF signal and the encoder signal (during ABS
activation) for oily road and initial speed of 1000 rpm. ............................................................. 61
Figure IV-10. Deviation of the back EMF signal from the reference signal (encoder signal) for
the dry road condition and an initial speed of 2300 rpm. .......................................................... 62
Figure IV-11. (A) Result of wavelet packet de-noising of the back EMF signal related to the dry
road condition with initial speed of 1000 rpm using db10 wavelet (B) Reference signal (Encoder
signal). ......................................................................................................................................... 64
Figure IV-12. Improvement of the Error Index for the de-noised back EMF signals by different
wavelets. ..................................................................................................................................... 66
Figure IV-13. Results of speed measurement from back EMF (during ABS activation) for a dry
road and an initial speed of 1000 r/min. .................................................................................... 67
Figure IV-14. Results of speed measurement from ABS sensor for a dry road and an initial
speed of 1000 rpm. ..................................................................................................................... 67
Figure IV-15. Error of speed measurement from back EMF for a dry road and an initial speed of
1000 rpm. .................................................................................................................................... 69
Figure IV-16. ABS sensor measurement error for a dry road and an initial speed of 1000 rpm. 70
Figure IV-17. Top figure: Result of WP de-noising of the back EMF signal related to the oily road
condition with initial speed of 1100 r/min using db7 wavelet. Bottom figure: De-noised back
EMF versus reference signal (encoder signal). ........................................................................... 71
Figure IV-18. Electronically commutated BLDC motor. .............................................................. 74
Figure IV-19. Equivalent circuit of BLDC machine (phase A). ...................................................... 75
Figure IV-20. General structural design of the braking system of an EV with four independently
driven BLDC motor In-Wheel hubs. ............................................................................................ 78
Figure IV-21. Comparison between ABS sensor (with 48 teeth) output and BLDC motor (with 8
pole pair) back EMF. ................................................................................................................... 78
Figure IV-22. Energy index for different road conditions and initial speeds. ............................. 83
Figure IV-23. Typical reference wheel speed measurements by optical encoder during a
complete experiment. ................................................................................................................. 84
Figure IV-24. Unprocessed output of ABS sensor. ...................................................................... 84
Figure IV-25. Speed measurement from ABS sensor using the standard frequency method (ABS
sensor measurements). .............................................................................................................. 85
Figure IV-26. Comparison between ABS sensor measurements and reference (optical encoder)
measurements during ABS activation. ........................................................................................ 86
Figure IV-27. The errors of ABS sensor measurements. ............................................................. 86
Figure IV-28. BLDC Back EMF (with 8 pole pairs) corresponding to Figure IV-24. ...................... 87
Figure IV-29. Top figure: Reference (optical encoder) measurements. Bottom figure: Absolute
values of the back EMF signal (amplitude signal) shown in Figure IV-28. .................................. 88
Figure IV-30. Peak signal (wheel speed estimations using the amplitude method) corresponding
to the amplitude signal shown in Figure IV-29. .......................................................................... 89
Figure IV-31. The errors associated with the wheel speed estimations shown in Figure IV-30. 90
Figure IV-32. Wheel-speed estimation from EMF of a BLDC with only two pole pairs using the
DWT method. .............................................................................................................................. 91
Figure IV-33. DC motor back EMF speed measurement. ............................................................ 92
Figure IV-34. DC motor back EMF error. ..................................................................................... 92
viii
Figure V-1. General structure of a braking system for an In-Wheel EV with four independently
driven motors and ABS sensors. ................................................................................................. 97
Figure V-2. Top figure: Typical wheel speed measurements (reference measurements) from the
optical encoder connected to the upper wheel. Bottom: The back EMF signal corresponding to
the top figure. ........................................................................................................................... 100
Figure V-3. The output of the ABS sensor corresponding to Figure V-2. .................................. 100
Figure V-4. Top figure: Measurements (reference measurements) from the optical encoder
connected to the upper wheel. Bottom figure: Absolute values of the ABS sensor output shown
in Figure V-3. ............................................................................................................................. 101
Figure V-5. Result of wheel speed measurement by the Frequency Method. ......................... 102
Figure V-6. Result of wheel speed measurement by the Amplitude Method. ......................... 102
Figure V-7. Fused signal by the OWA (dispersion) method. ..................................................... 104
Figure V-8. Fused signal by the Median method. ..................................................................... 105
Figure V-9. A Schematic diagram of the Sensor-fusion-based wheel speed measurement
system for a BLDC motor In-Wheel hub. .................................................................................. 106
Figure V-10. Reference (Optical Encoder) Measurements vs Speeds Calculated from ABS Sensor
Output using the Amplitude and Frequency Methods. ............................................................ 108
Figure V-11. Results of calculating wheel speed from three phases of BLDC using the amplitude
method. ..................................................................................................................................... 109
Figure V-12. Fused signal by the OOWA method. .................................................................... 111
Figure V-13. Fused signal by the POWA method. ..................................................................... 111
Figure V-14. Fused signal by the Median method. ................................................................... 112
Figure V-15. Comparison among different fusion methods, when fusing s1 to s4 signals. ...... 112
Figure V-16. Fused signal by the Median method (s1 to s8 signals are fused). ........................ 113
ix
List of Tables Table III-1. Parameters of the model. ......................................................................................... 38
Table IV-1. Error indices for back EMF and ABS sensor measurements. .................................... 70
Table V-1. Improvement of the MAE index for two-sensor fusion. .......................................... 103
Table V-2. . Improvement of the MAE index for three-sensor fusion. ..................................... 104
Table V-3. Results of calculating the MAE index for different wheel speed measurements. .. 109
Table V-4. MAE values calculated for OOWA, POWA and median methods where ABS sensor is
short circuited. .......................................................................................................................... 114
1
Chapter I
Introduction
2
I. INTRODUCTION In the past few years, the growing need to access sustainable energy and
solve environmental issues has motivated many governments to opt for
policies that reduce both oil dependency and carbon dioxide emission. To
this end, several countries have chosen to replace conventional Internal
Combustion Engine (ICE) vehicles with green vehicles as a measure to
increase the efficiency of their transportation systems. Electric Vehicles
(EVs) are the best options for a cleaner environment, as they produce zero
emissions and have great potential to use sustainable energy. However,
because their related technologies are not yet well developed, the
performance of EV systems and components can and should be further
optimized. The antilock braking system (ABS) is an important safety
component of modern vehicles. ABSs are designed to minimize the
vehicle's stopping distance in panic braking or other braking challenges
while maintaining steering control. In the past two decades, numerous
attempts have been made to improve the performance of ABSs for
conventional vehicles. As a result of advanced control methods, ABS
performance for conventional frictional brakes is close to optimal (see [1-
7] and references therein). On the other hand, issues related to ABS
design for EVs have recently gained attention [8-14], and the topic of
designing ABSs for In-Wheel EVs is yet to be fully investigated.
Electric Vehicles (EVs) make use of electric machines for propulsion. The
electric machine in an EV usually acts as an electric motor during
acceleration or cruising. The electric machine can also operate as an
electric generator during braking of the EV. This is called regenerative
3
braking, referring to the concept of recovering the kinetic energy of a
vehicle using the propulsion system [15]. The possibility of pure
regenerative braking during the activation of the ABS has been previously
considered by using the regenerative braking force as the only force for
decelerating the vehicle [16]. The simulation results in [16] show that
improved braking performance during ABS activation is possible with pure
regenerative braking. However, in practice, due to safety concerns, it is
still preferred to use the regenerative braking force in conjunction with
the frictional braking force (even during normal braking). Activation of the
ABS is rare, and the energy harvest is hardly economically justified.
Moreover, using regenerative braking during panic braking and the
activation of the ABS complicates the brake control and compromises
vehicle safety. As such, it is often preferred to disengage regenerative
braking during ABS activation [17], and decelerate the vehicle entirely by
the frictional braking system.
This thesis deals mainly with the development of antilock braking systems
for In-Wheel EVs. In-Wheel EV design is based on the idea of transferring
the propulsion system of the vehicle to its wheels. An assembly that
includes a separate electric motor as its propulsion system, as well as
other important components such as a frictional brake and a suspension
spring, is called an In-Wheel hub. Applying the traction force directly to
each wheel and simplifying the drivetrain enhances the energy efficiency
of In-Wheel EVs [18]. More importantly, the In-Wheel technology provides
the opportunity for superior motion control of the vehicle due to the fact
that electric motors can be controlled more precisely and significantly
faster than internal combustion vehicles [19, 20].
4
The availability of an electric machine at each corner of the EV also
provides additional freedom in the design of its braking system, which can
be improved by exploiting the regenerative back EMF of the electric
machines. One approach is to use the regenerative back EMF to recharge
the EV batteries, and produce regenerative braking torque to improve the
performance of the braking system. However, in almost all existing EVs, it
is common to disengage the regenerative braking torque during the
activation of ABS, and decelerate the vehicle entirely by the frictional
braking torque [17]. However, the regenerative back EMF of each In-
Wheel motor can still be effectively exploited to enhance the antilock
braking system of In-Wheel EVs. The assumption underpinning the
approaches introduced in this thesis, is that the regenerative back EMF
can be used to simplify and improve the wheel speed measurement part of
the ABS. This means that other parts of the ABS, including its controller,
remain intact and the ABS standard or advanced control strategies –– fully
tested for many years and legally approved –– can still be utilised [21-23].
This thesis is organised as follows. The literature review and problem
statements are given in Chapter II. Chapter III describes the experimental
ABS test rig and its modifications used for testing ABS for In-Wheel EVs in
this research. In Chapter IV, a sensorless ABS is proposed and developed
for brushed and brushless DC motor In-Wheel EVs. In Chapter V, a Sensor-
fusion-based ABS is proposed and developed for brushed and brushless
DC motor In-Wheel EVs. Finally, the conclusion and future works are given
in chapter VI. A list of publications based on this research is given at the
end of this thesis.
5
Chapter II
Literature Review
6
II. LITERATURE REVIEW
A. Introduction to ABS The braking action in a vehicle can be either a normal or a panic-braking
action. During the normal braking process, a vehicle is decelerated
according to the normal braking command from its driver. However,
sudden panic braking or braking on roads with low friction coefficients
results in the locking of the braking wheel in vehicles not equipped with
ABS technology. As a result, the locked wheel ceases to rotate which
increases the vehicle stopping distance and makes the steering of the
vehicle impossible.
The ABS is a safety-critical component in modern vehicles. Its role is to
improve a vehicle's manoeuvrability and decrease its stopping distance in
challenging braking scenarios (see Figure II-1). ABSs were introduced to
the aviation industry in the late 1920s and were later developed for cars.
The improved performance of the ABS for conventional vehicles is of
significant importance, and has received substantial attention during the
past decade [1-4, 6, 7, 24, 25].
A conventional ABS consists mainly of an actuator, an ABS sensor, and a
controller. The role of the ABS sensor is to provide the ABS controller with
continuous wheel speed measurements during ABS activation. The
controller generates a suitable control signal based on the speed. This
signal is fed back to the actuator, which regulates the braking force
accordingly [22]. The actuators that are used in conventional ABSs are
hydraulic solenoid valves that operate in three operation modes: brake-
pressure increase, hold, and decrease [26, 27]. Consequently, the locking
7
of a wheel can be prevented by appropriately increasing holding, or
decreasing the braking force.
Figure II-1. The role of ABS.
As discussed in [22], the most common approach to control modern ABSs
is slip control. The aim of slip-based control is to maintain the slip ratio of
each wheel in the stable region that maximises the braking force via
maximising the friction coefficient between the wheel and road. The slip
ratio is defined as:
(2.1)
where V is the vehicle speed, is the radius of the wheel, and is the
rotational speed of the wheel. As discussed in [7], the slip-based control is
an attractive approach for the control of ABSs. It guarantees the
8
uniqueness of the steady-state solution as well as the asymptotic stability
of the closed-loop system with a proportional slip controller.
1) ABS sensor
The common technology for measuring wheel speed in an antilock
braking system (ABS) is to use a magnetic wheel speed sensor (WSS),
often referred to as the ABS sensor. This sensor usually consists of a
rotating toothed ring called the ABS ring, a permanent magnet, and a
winding called the pickup that produces a voltage signal that contains the
wheel speed information. The ABS ring, also called tone, trigger, or gear
wheel, is an essential component of the WSS and generates the speed
signal by modulating the magnetic field between the magnet and the
winding [22].
A typical commercial ABS sensor used in sedan cars is shown in Figure II-2.
The ABS ring is rotating with the vehicle wheel at the same rotational
speed. The rotational motion of the ABS ring in the magnetic field of the
permanent magnet induces a voltage in the winding. This voltage contains
the wheel speed information and is used in conventional ABSs to estimate
the wheel's rotational speed.
9
Figure II-2. A commercial ABS sensor (made by Repco) used in sedan cars.
Figure II-3. A single tooth of the ABS ring in the proximity of the winding.
a) Modelling ABS sensor output
As discussed in [21], the rotation of the ABS toothed ring in front of the
ABS sensor winding induces a sinusoidal voltage in that winding. This
voltage is composed of consecutive cycles for one complete rotation of
the ABS ring, where is the number of teeth of the ABS ring (see Figure
II-3). The magnetic flux in the proximity of the ABS sensor winding can be
modelled by:
10
(2.2)
where is the constant flux produced by the permanent magnet of the
ABS sensor, is the number of teeth in the ABS ring, and is the position
of the ABS ring. According to Faraday’s Law, the electromotive force
induced on the winding can be calculated as [21]:
(2.3)
where is number of turns in the winding and is the angular velocity of
the ABS ring. The above equation can be simplified as:
(2.4)
where is a constant value. Although the above equation shows that both
amplitude and frequency of the ABS sensor output voltage are directly
proportional to the rotational speed of the ABS ring, the frequency
information is commonly used for wheel speed measurement. As such, for
comparison, we have also used the frequency method in our experiments
to calculate the wheel speed from the ABS sensor output [21].
b) Standard ABS sensor wheel speed measurement
A typical output voltage of a commercial ABS sensor (made by Repco Ltd)
is shown in Figure II-4. The output of an ABS speed sensor is an analogue
signal, and since ABS modules and electronic control units (ECUs) in cars
can process only digital inputs, those signals need to be digitised [28]. The
speed of the wheel is proportional to the frequency of the ABS sensor
11
signal, and the frequency is measured to determine the speed. As
discussed in [22], to measure the frequency at every sampling point, the
duration between two adjacent signal zero crossings is measured in terms
of the number of samples. The corresponding frequency at the th point
is proportional to the inverse of this duration
(2.5)
where is the time duration between two adjacent zero crossings,
which is measured by counting the number of samples between those
crossings.
Figure II-4. Typical output voltage of a commercial ABS sensor (made by Repco Ltd).
12
Figure II-5. Result of converting output of the ABS sensor (shown in Figure II-4 ) to frequency.
Figure II-5 shows the calculated frequencies for the ABS sensor output
depicted in Figure II-4. It is important to note here that the ABS sensor
output at lower speeds is susceptible to noise, and large measurement
errors are usually observed in low-speed measurements. To remove those
erroneous measurements, we implemented a robust filtering method that
uses a threshold that can find and replace these erroneous measurements
with their estimated values. The final step for calculation of wheel speed
measurement is to normalise the amplitudes. The final corrected speed
signal corresponding to Figure II-5 is shown in Figure II-6. The above wheel
speed calculation is the standard method of measuring wheel speed from
the output voltage of the ABS sensor. As such, in the experiments
throughout this thesis, we have used the same method for calculating
wheel speed from the ABS sensor. We will refer to the above wheel speed
measurements as ABS sensor measurements henceforth.
13
Figure II-6. Corrected speed signal from the ABS sensor output.
c) ABS sensor disadvantages
As discussed in [22], the ABS sensor has a simple and rugged design.
However, the commonly used technology has the following
disadvantages:
The ABS sensor, due to the size of the ABS ring, is relatively bulky
compared with most sensors. Typical ABS rings have a diameter
range of 44–115 mm and a width range of 6–30 mm
The ABS ring is vulnerable both to environmental factors such as
rust and dirt and to operational factors such as wheel hub
inspection or repair procedures
The size of the air gap between the ABS ring and the magnetic
field affects the accuracy of the sensor. This is difficult to adjust,
14
and in some designs the whole wheel hub has to be replaced
when the air gap is unbalanced
ABS speed measurements are susceptible to mechanical faults in
the wheel-bearing assembly
ABS speed measurements are susceptible to wiring faults (e.g. an
open/short circuit or changes in wire resistance).
We will show that it is possible to eliminate the conventional ABS sensor
for In-Wheel EVs. The wheel speed is alternatively estimated from the
back EMF of brushed and brushless electric motors at each corner of In-
Wheel EVs. The proposed sensorless ABS overcomes the aforementioned
disadvantages and reduces the costs associated with the installation and
maintenance of conventional ABS sensors for In-Wheel EVs. Therefore, an
important contribution of this thesis is that the proposed sensorless ABS
has been developed and fully tested using actual ABS hardware and
motors.
2) Intelligent ABS As discussed in [22], slip-based controllers are the most commonly used
controllers of ABSs but have two main drawbacks. First, introducing only
one fixed reference value for all road conditions would yield suboptimal
results. This means that a suitable reference slip should be determined for
different braking scenarios based on road conditions [2]. In order to
improve the performance of the ABS on different road conditions, this
thesis introduces efficient methods of identifying road conditions. Those
methods are based on the wavelet analysis of the back EMF signal of
15
different brushed and brushless motors in In-Wheel EVs [15, 21, 22]. The
methods not only extract important features that identify the road
condition but also detect sudden changes in the road condition in
challenging braking scenarios.
The second major drawback of slip control for ABS is its sensitivity to
erroneous slip measurements [7]. The accurate measurement of the wheel
speed is crucial to the slip ratio calculation, and plays an important role in
the optimum performance of the ABS. The overall accuracy of slip
measurements depends on the accuracy of both wheel and vehicle speed
measurements. Equation (2.1) shows that the accuracy of wheel speed
measurements directly influences the accuracy of slip calculations. The
accuracy of wheel speed measurements also influences the estimation of
vehicle speed. In practice, the vehicle speed is not directly measured for
the ABS application [7] but is estimated from the wheel speed (e.g. [28,
29]). As such, the accuracy of vehicle speed also depends (indirectly) on
the accuracy of the wheel speed measurements. This means the overall
slip measurement accuracy would be significantly influenced by the
accuracy of wheel speed measurements [22].
The importance of accurate wheel speed measurement has motivated
brake manufacturers to produce accurate WSSs using precision
technologies. Several attempts have also been made to improve the
accuracy of existing WSSs using signal processing methods. For instance, a
method was proposed in [30] to compensate for the mechanical
inaccuracy of conventional ABS sensors. Their analysis showed that the
irregularities of the tooth and gap width have the largest influence on the
16
speed-signal quality of conventional sensors. In addition to mechanical
inaccuracies, noise can affect the accuracy of wheel speed measurement.
A frequency-domain least-mean-square adaptive filter was used in [31] to
improve the wheel speed measurement. To further mitigate the effect of
noise, the same author also proposed using an adaptive line enhancer
algorithm to predict the response of a WSS buried in a broadband noise
[32]. The experimental results of the aforementioned two works showed
that in practice where noise is present, it is necessary to further process
the output of common WSSs to achieve the required accuracy [22].
The effect of noise on the performance of a slip-based ABS with
proportional controllers was studied in [7]. The analysis showed that
noise sensitivity is a critical aspect of slip control in an ABS. In practice,
however, the on-off (bang-bang) control is commonly used. In order to
study the effect of noise on the performance of practical ABS controllers,
we simulated the noise effect on the stopping distance of a vehicle with
an existing ABS model (sldemo_absbrake.mdl available in Matlab version
R2010b). The effect of noise is simulated by the introduction of several
band-limited white-noise signals with different powers to the slip
measurements in the ABS model. Figure II-7 shows the stopping distance
for different noise powers, and that the stopping distance increases as the
power of the noise is increased. This can be explained by considering the
nonlinear operation of the on-off controller. The erroneous slip
measurements due to noise result in an incorrect command from the
controller. When the noise amplitude is greater than the amplitude of the
error in the closed-loop control system with an on-off controller, an
erroneous maximum control command will be issued instead of the
17
minimum control command (or vice versa). As a result, an insufficient
braking force is applied to the wheel, and the stopping distance increases
because the friction has deviated significantly from its maximum value.
The fact that noise can increase the stopping distance emphasises the
importance of accurate wheel speed measurement for ABS.
Figure II-7. Effect of noise (slip measurement errors) on the stopping distance
of a vehicle with ABS (with On-Off controller).
The words 'intelligent' or 'enhanced' in [15, 21-23, 33] and in this thesis
signify that the proposed ABSs in those works would improve ABS
performance by increasing the accuracy of wheel speed measurement,
and conducting road identification to keep the ABS in its stable region. It
will be shown in this thesis that wavelet and data fusion approaches
achieve those improvements.
18
B. Introduction to Wavelets Conventional spectrum analysis transforms such as Fourier provide the
spectral content of a given signal. Although the Fourier transform is able
to analyse the frequency contents of a signal, it lacks the sophistication to
specify the instant of time when the change of frequency pattern occurs.
Wavelet analysis [34-38] can be used to model and extract transient
features in the time-frequency domain. Identifying the transition time is of
particular importance in the ABS application and traditional signal
processing methods such as the Fourier transform do not provide a time-
frequency decomposition suitable for real-time feature extraction [15].
1) Continuous Wavelet Transform (CWT) The Continuous Wavelet Transform (CWT) of signal is given by:
√ ∫ (
)
(2.6)
where is a complex conjugate of a wavelet function and and are
scale and shift values, respectively. The scale and shift parameters in the
continuous wavelet transform are real values. This enables the effective
time-frequency decomposition of a signal that can be used for local
feature extraction [21].
19
2) Discrete Wavelet Transform (DWT) The Discrete Wavelet Transform (DWT) of a discrete signal [ ] is given
by:
[ ] ∑ [ ] [ ]
(2.7)
where represents sample points of the signal and and ( )
are scale and shift parameters, respectively. Discrete wavelet transforms
can be considered as the discrete version of continuous wavelet
transforms with a dyadic time-frequency decomposition structure that
provides low- and high-frequency components of a given signal at each
decomposition level. The low- and high-frequency components are
referred to as Approximations ( ) and Details ( ) at level , respectively
(see Figure II-8). While Approximations of a given signal represent the
smooth variations, the Details are related to abrupt changes in the signal
[21].
The Discrete Wavelet Transform (DWT), like the Continuous Wavelet
Transform (CWT), provides a time-frequency decomposition of a given
signal and is able to specify frequency bands ( or s) that carry
particular pieces of information related to a specific phenomenon [15].
20
Figure II-8. Implementation structure of the Discrete Wavelet Transform (DWT).
Haar wavelet is the first wavelet that was introduced by Alfred Haar, a
Hungarian mathematician. The Haar wavelet’s mother wavelet function,
is shown in Figure II-9 and can be compared with Dmey and
daubechies (db2) mother wavelet functions in the same figure.
Figure II-9. Mother wavelet functions of Haar, db2 and dmey wavelets.
21
3) Wavelet Packet (WP) The Wavelet Packet (WP) analysis can be considered as an extension of
the DWT. Both techniques include decomposing a given signal into bands
of higher and lower frequency in each level of decomposition. Two
frequency bands in each level of decomposition are referred to as
Approximations and Details representing the lower- and higher-frequency
components, respectively. However, the DWT ignores the higher-
frequency band for the next decomposition step and only considers
decomposing the lower-frequency band (Approximations) in each
decomposition level. On the other hand, WP continues decomposing both
the lower- and higher-frequency bands in all decomposition levels, as
depicted in Figure II-10 [22, 33].
Figure II-10. Implementation structure of DWT and WP;
while WP decomposes both the higher (Details) and the lower frequency
(Approximation) components at each level of decomposition, the DWT proceeds to
decompose only the lower frequency bands.
Both DWT and WP are suitable candidates for the analysis of
nonstationary signals. As the frequency content of the back EMF of the In-
Wheel EV changes with regard to time (e.g. due to a sudden change in
22
road condition), wavelet analyses can detect sudden changes in the
frequency pattern through their capacity for time-frequency
decomposition.
23
C. Introduction to Data Fusion Data fusion deals with integration of data from multiple sources of
information to provide a better understanding of the environment [39].
Data fusion is one of the best ways to provide useful information with a
minimum of uncertainty. The main advantages of data fusion include [40,
41]:
Efficient use of redundant information: Multiple sensors or
sources of information may provide overlapping or redundant
information (see Figure II-11). These data can be efficiently fused to
reduce uncertainty by improving accuracy, robustness, reliability
and fault-tolerance.
Effective use of complementary information: Data fusion can also
effectively reduce uncertainty by providing more comprehensive
descriptions of the environment through the use of complementary
information that might not be available in every individual source
of information (see Figure II-11).
Timeliness: The actual operational speed of individual sensors or
possible processing parallelism as part of the fusion process can
provide more timely information.
Reduction in cost: It is possible to fuse data from multiple,
inexpensive sensors, with significantly less overall cost, to achieve
better accuracy. In addition, the increased accuracy improves
efficiency which further reduces costs.
24
Figure II-11. Schematic diagram of redundant and complementary information.
Data fusion finds numerous applications in a variety of domains including
navigation, robotics science, measurement science, vehicular technology
etc. [28, 40, 42-47]. Data fusion has also been used to improve the
performance of the conventional antilock braking system (ABS). For
instance, the sensor-data fusion approach was used in [28] to improve the
accuracy of vehicle speed estimation. In that study, the wheel speed
measurement (calculated from the frequency of the ABS sensor) is fused
with vehicle body acceleration information in order to more accurately
estimate the vehicle's velocity. Similarly, the sensor-data fusion approach
was used in [44] for tyre-road friction estimation to improve ABS
performance [23].
25
1) Ordered Weighted Averaging (OWA) The Ordered Weighted Averaging (OWA) operators were originally
introduced by Yager as a solution for the problem of 'aggregating
multicriteria' which combine multiple criteria into one fused format [48].
An OWA operator of dimension n is a mapping F: Rn → R given by:
∑
(2.8)
-r
where σ is a permutation that rearranges the elements such that
The are all non-negative weights
with the following constraint [23]:
∑
(2.9)
OWA operators have proved to be useful means of aggregation in
numerous applications [46, 49, 50], providing a class of parameterised
operators. They include many well-known operators such as minimum,
maximum, mean, and median. Some important characteristics of OWA
operators are their commutativity, idempotency and monotonicity. Also,
any aggregation that results from using the OWA operator is always
between the minimum and maximum [23]. This fact inspired the
introduction of a degree of maxness (initially called orness) in [48] as:
∑
(2.10)
26
-r
Note that the maxness of the minimum operator is equal to zero
(maxness(1,0,…,0) 0), the maxness of maximum operator is equal to one
(maxness(0,0,…,1) 1) and the maxness measure always lies in the unit
interval [23].
The associated weights of OWA operators can be determined using
different approaches. One approach is based on defining a dispersion
measure that can be used to find the weights based on the entropy
concept [48]. The algorithm for finding the OWA weights for the above
approach, for a given maxness, is provided in [51] and the dispersion
measure is defined as:
∑
(2.11)
Another approach to obtain the OWA weights using the maxness measure
is to use an exponential class of OWA operators [52]. The optimistic
exponential OWA assigns the following weights to the OWA operator:
(2.12)
while the pessimistic exponential OWA weights are [52]:
(2.13)
27
where belongs to the unit interval and is related to the maxness
measure [23].
28
D. Summary of Chapter II
This chapter reviewed wheel speed measurement for the antilock braking
system (ABS) as well as introducing a model for the ABS sensor output
using fundamental principles underpinning the measurement mechanism
of the commercial ABS sensor.
In addition, it investigated the important role of accurate wheel speed
estimation and road identification for optimal performance of an ABS. It
showed that inaccurate wheel speed estimations significantly increased
the vehicle's stopping distance during ABS activation. The term 'intelligent
ABS' is used in this thesis to signify that the proposed ABSs would improve
ABS performance by increasing the wheel speed measurement accuracy
and by conducting road identification to keep the ABS in its stable region.
This chapter also briefly introduced the fundamentals of two powerful
approaches: wavelets and data fusion. These will be used in chapters IV
and V for the realisation of accurate wheel speed estimation and road
identification for the ABS of In-Wheel EVs.
29
Chapter III
Experimental System
30
III. EXPERIMENTAL SYSTEM This chapter describes details of the standard ABS test rig (made by Inteco
Ltd) as well as required modifications to develop an ABS test rig for In-
Wheel braking systems. In addition, it explains the computer simulation
approach based on using the modified ABS test rig model.
A. Standard ABS Test Rig
Figure III-1. ABS experimental setup [53] for conventional brakes (picture available at www.inteco.com.pl).
The standard ABS experimental test rig used as a base framework of our
experiments is shown in Figure III-1 [53]. The test rig consists of the
following hardware components:
Car wheel: This wheel simulates the wheel motion
The wheel simulating vehicle motion
31
Two optical encoders
Brake system: disk Brake, brake motor
A damper
Driving motor: This motor is coupled to the wheel simulating
vehicle motion and directly drives this wheel
Data acquisition system.
The test rig consists of two wheels which simulate the vehicle and wheel
motion. Each of the two wheels is connected to an incremental optical
encoder with a resolution of up to 2048 cycles per revolution (CPR). The
encoder connected to the upper wheel is used to measure the wheel
speed ( ), and the encoder connected to the lower wheel is used to
measure the vehicle speed (V). As a result, the slip ratio can be calculated
from the wheel and vehicle speed ( and V) according to (2.1) , and the
control of the ABS, which is commonly based on the notion of keeping the
slip ratio in a given (stable) range, can be realised [22].
A DC motor controlled by pulse-width modulation (PWM) is coupled to the
lower wheel and directly drives this wheel. The upper wheel is driven by
friction contact with the lower wheel. The braking phase starts when the
upper wheel reaches a predefined speed. At that point, the DC drive is
turned off and the upper wheel can be decelerated by a braking disk
system [22].
The optical encoder signals were captured by a multipurpose digital I/O
(RT-DAC/USB data acquisition) board which communicates with the
computer through a power interface shown in Figure III-2. All necessary
32
logic circuits to activate and read the encoder signals, to generate the
appropriate control signals, and to drive the DC motor by PWM (connected
to the lower wheel) are implemented using the Xilinx chip of the RT-
DAC/USB board [54].
Figure III-2. ABS test rig and data acquisition systems.
33
B. ABS Test Rig Modifications To examine the feasibility of developing an intelligent antilock braking
system (ABS) for different brushed and brushless motor In-Wheel EVs, we
modified the standard ABS experimental test rig to include regenerative
braking. This was achieved by coupling electric motors to the upper wheel
of the standard test rig.
Figure III-3. Main steps of adding a commercial ABS sensor to the ABS test rig. The
wheel hub with an integrated ABS sensor is shown in the bottom part. The
disassembled ABS sensor is shown in the middle, and the coupling of the ABS ring to
the test rig is shown in the top part.
In addition, to show that the accuracy of the proposed wheel speed
measurement methods in this thesis, are adequate in practice, the
34
accuracy of wheel speed estimations from the back EMF signal was also
compared with an actual ABS sensor used in a commercial sedan. To
compare the accuracy of those measurements, we added a commercial
ABS sensor (made by Repco Ltd for a family sedan) to the existing test rig.
The ABS sensor is not a stand-alone item and is commonly integrated into
the wheel hub assembly of the vehicle.
To install the ABS sensor on our test rig, the ABS sensor and its ring were
first removed from the wheel assembly. A new coupling system was
designed so that the ABS ring would rotate with the upper wheel shaft
(and the regenerative motor). The main steps of this modification are
shown in Figure III-3. As a result, the wheel speed can be estimated and
compared with three different techniques: the conventional ABS sensor
made by Repco; the test rig’s optical encoder (as the reference); and new
methods proposed in this thesis that are based on using back EMF of
different electric motors. Figure III-4 shows the installation details of a
brushed DC motor and conventional ABS sensor on the test rig.
35
Figure III-4. Installation details of a DC motor and conventional ABS sensor on the
standard ABS test rig.
An additional Daqbook/112 data acquisition system was used to capture
the back EMF signal of the dc motor (connected to the upper wheel) as
well as the signal from the ABS sensor. The Daqbook/112 can sample
input signals with a maximum sampling rate of 100 kHz. Although a
typical ABS system needs a new measurement every 7 ms [30], the ABS
speed sensors need to be sampled at a much higher rate to achieve good
measurement accuracy [55]. Our experiments showed that the ABS speed
sensors need to be sampled at around 5 kHz to achieve good accuracy.
36
C. Computer Simulations An effective approach in planning experiments with the modified ABS test
rig is to test the effectiveness of wavelet and/or data fusion approaches on
simulated back EMF signals, before conducting actual experiments.
Computer simulations provide the fastest and most convenient approach
for those purposes.
Figure III-5. Schematic diagram of the experimental ABS test rig for conventional brakes.
A schematic diagram of the experimental ABS test rig developed by
Inteco Ltd [54] is presented in Figure III-5.
The following system of equations models the motion of those wheels
[54].
(3.1)
37
(3.2)
In the above equations, is the wheel slip and is defined as:
{
(3.3)
While s, s1 and s2 are auxiliary variables defined by
s sgn r x r1x1 (3.4)
s1 sgn x1 (3.5)
s sgn x (3.6)
and the rest of parameters are defined in table 1.
38
Table III-1. Parameters of the model.
Total Fn is calculated as:
Fn g s1 1 s1 10 d1x1L sin s cos
. (3.7)
The variations of friction coefficient with respect to wheel slip for dry
asphalt and icy road conditions are shown in Figure III-6 [56].
39
Figure III-6. Friction coefficient vs. wheel slip for asphalt and icy road conditions.
The friction coefficient can be approximated as a nonlinear function of the
wheel slip according to the following formula which is developed by [53]:
(3.8)
To determine the response of the electric drive during the activation of the
ABS, the above model of the experimental test rig was simulated using
MATLAB Simulink software. The numerical values that were used in our
experiments were from [53].
40
Figure III-7. A typical simulation model in MATLAB Simulink Software.
A typical Simulink model of the simulations used in this thesis is shown in
Figure III-7. The main purpose of those simulations is to generate
simulated back EMF of different brushed and brushless motors. For
example, the simulation model shown in Figure III-7 is used to generate
back EMF of DC motor during ABS Activation. The typical simulation model
can be easily modified to generate the back EMF of any electric machine
by replacing the DC motor with other motors. The mechanical (frictional)
braking torque is controlled by a bang-bang controller consisting of a sign
function, first-order lag, an integrator and a gain. The error between the
desired slip and the actual slip is fed to the sign function generating an
on/off signal. This signal is then fed to the hydraulic lag (which models the
hydraulic actuator). The resulting signal is then integrated to yield the
braking pressure and is finally multiplied by a gain (which represents the
piston area and radius with respect to wheel) to deliver the mechanical
(frictional) braking torque (M1). The desired slip for dry road conditions
was assumed to be 0.2 in our simulations.
41
D. Summary of Chapter III The standard ABS test rig used as a base framework of our experiments
was introduced in this chapter. In addition, the required modifications to
develop an ABS test rig for In-Wheel braking systems were described. The
mathematical model of the system was also simulated with Matlab
software. The computer simulation with Matlab software enabled pre-
analysis of several motor back EMF signals prior to conducting actual
experiments with the modified ABS test rig.
42
Chapter IV
Intelligent Sensorless ABS
43
IV. INTELLIGENT SENSORLESS ABS As discussed in [22], the In-Wheel technology uses separate electric
motors at each corner of an electric vehicle (EV) and this propulsion
system is on the verge of exponential growth. The availability of an
additional regenerative braking torque at each corner of In-Wheel EVs
provides great opportunity for advanced and novel designs for their
braking systems.
The ABS is normally installed at each corner of the vehicle to maximize
safety. The wheel speed sensor (WSS), is a crucial and relatively costly
component of the ABS. Its role is to provide the ABS controller with
measurements of the wheel speed. This chapter introduces and
elaborates on the idea of making this component redundant at each
corner of In-Wheel EVs.
The sensorless ABS idea is based on the fact that, although the
regenerative braking toque of the In-Wheel EV is not used during the
activation of the ABS, the electric machine remains in action during the
braking phase and can provide valuable information for the
implementation of ABS. It will be shown in this chapter that the motor
output can be used not only to measure the wheel speed and remove the
WSS completely (called sensorless ABS) but also to make the ABS
'intelligent' by accurate wheel speed measurement and road
identification. The performance of the proposed wheel speed
measurement system is extensively tested and compared with the ABS
sensor measurements of a real-world traditional car. Furthermore, the
44
performance of the sensorless ABS has been simulated and tested for
different road conditions.
Figure IV-1. Role of sensorless ABS in saving physical space in the In-Wheel hub.
The In-Wheel design for an EV is advantageous in many respects [20, 57-
59]. However, In-Wheel technology involves the integration of several
important components such as suspension, braking, and propulsion
systems in a small compartment. Figure IV-1 shows that a sensorless ABS
provides an added advantage in terms of saving physical space. In addition,
ABS sensors require separate wiring to transmit speed information. This
additional wiring can be influenced by other electrical components,
particularly in an In-Wheel EV where a powerful electric motor is included
in the wheel assembly with its potential to disturb the ABS sensor signal.
45
As such, the sensorless ABS omits a potential source of malfunction for In-
Wheel EV brake systems, and the removal of the speed sensor would
simplify maintenance. It is important to note here that the wheel assembly
of In-Wheel vehicles is substantially more complicated than the
conventional wheel hubs, which makes the sensor maintenance more
time-consuming and costly than other vehicles. As such, the proposed
sensorless ABS simplifies the design of ABS for In-Wheel EVs and
significantly reduces their final and on-going maintenance costs by
eliminating the need to install separate conventional ABS sensors [22]. The
rest of this chapter develops and realises a proposed system for brushed
and brushless In-Wheel EVs.
46
A. Intelligent Sensorless ABS for Brushed In-Wheel EVs As discussed in [22], the motor drives for EVs can be classified into two
groups: commutator motors and commutatorless motors. Commutator
motors primarily include traditional DC motors which need commutators
and brushes to supply current to the armature. Although this method of
operation makes the maintenance of these motors difficult, the DC motor
drives have been 'prominent in electric propulsion systems' due to their
mature technology and simple control [60]. In this section, the feasibility
of the sensorless ABS is investigated for DC motors, which are the most
mature and low-cost electric motor technology available. In addition to
the low cost of the DC motors, the required power electronic devices and
control methods of the DC motor are also inexpensive and mature, which
still makes the DC motor a good candidate for low cost EVs [61-66].
It should also be noted that the application of the in-wheel technology is
not limited to four-wheel sedan EVs. Electric wheelchairs, motorcycles,
scooters and so on are also using the in-wheel technology as their
propulsion system. The markets for those vehicles are extremely cost
sensitive, and significant efforts worldwide are spent on reducing the cost
of those vehicles. Simple DC motors are also currently the propulsion of
choice for those vehicles, and the development of low-cost ABS
technologies is likely to expand the ABS usage in those vehicles.
In this section, it will be shown that the brushed DC motor embedded
inside each wheel of an EV can play the role of a conventional ABS sensor
during braking. This means that, for an In-Wheel EV, with four
47
independently driven wheels, the speed information is readily available
during the activation of the ABS without the need to install a separate
sensor.
Reducing the cost and complexity of the in-wheel design has been the
main goal of this work, and the proposed sensorless ABS would decrease
the cost of the vehicle by omitting the need for separate ABS sensors at
each wheel. Automotive part manufacturers are always under pressure to
reduce the cost of their units, and the significance of developing
sensorless ABS arises from its potential of reducing the final cost of the
ABS implementation. The electric car market is yet to be fully established,
and their manufacturers’ success is likely to depend on the E s’ final cost.
In addition to the low cost of DC motors and their required electronic drive
systems, the DC motor has a high starting torque which is a suitable
characteristic for EVs [62]. The decoupling of flux and torque is another
interesting feature of the DC motor [61]. Currently, a major concern in
designing an EV is its recyclability. Recycling DC motors has been easier
and more economical compared with brushless motors (such as
permanent-magnet synchronous motors and brushless DC motors) that
use a permanent magnet to provide the required electromagnetic field.
Expensive rare-earth permanent magnets used in brushless electric
machines do not only increase the motor cost, but are also hard to
recycle. The recycling of magnets with the highest energy product (e.g.
NdFeB) is difficult, and in case of corrosion or demagnetisation, the
recycling process is uneconomical [67].
48
Consider the following equations, which describe the dynamics of a DC
machine:
(4.1)
(4.2)
(4.3)
where is the speed of the armature (same as the wheel angular
velocity), is the flux per pole, is a constant, is the resistance of
the armature circuit, is the armature voltage, is the armature
current, and is the motor torque. As it was discussed earlier, the
regenerative braking torque is normally eliminated during the activation
of the ABS ( ), and equation (4.1) simplifies to
(4.4)
Equation (4.4) shows that the armature voltage (same as back EMF
voltage) is linearly proportional to the speed of the armature (or the
wheel speed). In practice, this relationship is affected by noise. However,
we show that it can be used to estimate the wheel speed and produce
measurements that are more accurate than those of commercial ABS
sensors (a magnetic WSS that is added to our test rig).
In addition to the aforementioned advantages of the sensorless ABS, the
proposed sensorless ABS design for a brushed DC motor In-Wheel hub is
capable of estimating the wheel speed with better accuracy in comparison
with conventional ABS sensors. The proposed sensorless measurement
49
also has less computational complexity because the back EMF is linearly
related to the wheel speed. Since the back EMF signal also contains
information about road condition during the braking phase, the processing
of the back EMF in the proposed sensorless ABS allows the wheel speed
measurement and road identification to be conducted simultaneously.
The general architecture of a braking system of an EV with four
independently driven brushed DC motor In-Wheel hubs is shown in Figure
IV-2. The braking system includes a central brake controller, four local
controllers, and a communication network. The information from each
brushed DC-motor in-wheel hub is transferred to the central brake
controller embedded in the ECU of an EV via its signal transmission and
communication network. The central brake controller would then interact
with each local controller to generate an appropriate control signal for
the operation of ABS at each wheel.
50
Figure IV-2. General architecture of a braking system of an EV with four
independently driven, DC-motor in-wheel hubs.
Sensorless techniques for brushed DC motors can be classified into two
groups [68]. The first group is based on the dynamic model of the DC
motor (e.g. [69]), and the second group is based on monitoring the ripple
component of the motor current (e.g. [70]). The problem with model-
based methods that use motor parameters such as resistance, inductance,
and EMF is the validity of the model for different operating conditions. As
the operating condition of the motor changes, the motor speed would be
measured with uncertainty. There are some methods for estimating these
parameters dynamically; but this would lead to a nonlinear model and
increase the computational cost [70].
51
To improve the performance of the ABS for brushed DC motor In-Wheel
hubs, road identification and de-noising methods based on the wavelet
analysis of the DC-motor back EMF signal are used in this thesis. The DWT
and WP algorithms for the back EMF de-noising (for accurate wheel speed
estimation) and feature extraction (for road identification) were
implemented as follows.
The first step (both for DWT and WP) was to decompose the back EMF
signal in order to achieve approximation (Ai) and detail coefficients (Di) at
desired frequency bands, as described in the section II-B-2. The de-noising
of the signal was then carried out by reconstructing a more accurate
signal by modifying the decomposition coefficients from WP. The road
condition features were extracted by calculating the energy of the
decomposition coefficients from DWT. A change in the energy of the
coefficients is shown to indicate a change in road condition. The proposed
algorithms can be implemented in real-time using any of the hardware
signal processing platforms such as DSP, FPGA, and ASIC. However, FPGA
was shown to be an appropriate platform for the implementation of the
type of algorithms proposed here due to its architecture freedom and low
cost [71].
1) Experimental Results and Discussions
a) Sensorless Road Identification
The road identification method based on analysing the back EMF of DC
motor In-Wheel hub using the DWT technique were first examined by
analysing a number of simulated back EMFs. To this end, the typical
simulation model in the Simulink software (shown in Figure III-7) was
52
modified and then used to generate several simulated back EMFs related
to different road conditions (see Figure IV-3). The simulation model
shown in Figure III-7 was also designed to enable generating simulated
back EMFs related to a sudden change in road condition as well. This was
carried by changing the tire-road friction coefficient according to the
change in road condition.
Figure IV-3. Modified version of the simulation file shown in Figure III-7.
The feasibility of detecting a sudden change in road condition during
activation of ABS was then examined by testing the performance of the
DWT technique on those simulated back EMFs. The emphasis of those
analyses was to investigate the possibility of detecting the frequency
bands containing the information related to change of road conditions by
DWT decomposition of the back EMF signal of the DC motor In-Wheel
hub.
The wavelet decompositions of the DC motor armature voltage signals are
shown in Figure IV-4. In Figure IV-4B, the road condition is changed 15 m
53
after the start of the braking phase. As shown by d2 and d3 plots, the
high-frequency components of the armature voltage signal
decomposition change as road surface changes from dry asphalt to
ice(the amplitude of the high frequency components (d2 and d3)
decreases where the road condition changes)”
As such in this thesis, the back EMF signal energy in these two specific
frequency bands is promoted as an index for road identification during
emergency braking [15, 22].
54
Figure IV-4. Results of decomposing the armature voltage (in volts) by Haar wavelet,
(A) dry asphalt (B) road surface changes from dry asphalt to icy road 15 m after the
start of the braking phase.
55
The back EMF signal shown in Figure IV-4 is related to a braking scenario
in which the slip ratio measurements are noise free. In practice, however,
the external disturbances (measurement noises) can influence the system
performance. To investigate the effect of noise on the performance of the
above road identification method based on DWT, various amounts of
white noise were added to the slip measurement as shown in Figure IV-5.
The effect of noise on the back EMF signal in one of those simulations is
shown in the top part of Figure IV-5B. The figure shows that introducing
noise to the system significantly changes the armature voltage and
therefore, detection of changes in road condition by direct analysis of this
quantity would not be robust. However, Figure IV-5B shows that noise has
only slightly altered the pattern of d2 and d3 compared with Figure IV-4B.
Irrespective of the amount of noise, the amplitude of the high-frequency
components (d2 and d3) decreases where the road condition changes
from dry asphalt road to icy road. This shows that robust change
detection algorithms can be devised by analysing the higher frequency
bands (d2 and d3) to detect changes in road condition [15, 22].
56
Figure IV-5. (A) The additive noise signal introduced to the system (B) Effect of noise
on the back EMF signal.
To validate the proposed road identification method, we conducted
several experiments with two different road conditions (dry road and
slippery road) to produce a real back EMF signal for a braking scenario
involving the change of road condition. Since it is difficult to test the icy
57
road condition in a laboratory, we replicated the slippery road condition by
covering the surface of the lower wheel (that represents the road surface
in the ABS test rig) with oil. The rationale for covering the road with oil
instead of ice is that, in both situations, friction coefficients are similar and
both are significantly lower than the dry road friction coefficient. Two back
EMF signals (related to the start of braking on a dry road followed by
braking on an oily road condition) were concatenated to represent the
back EMF signal of braking on a road with a sudden change in its surface
condition. This signal was then analysed by DWT to examine the
aforementioned road identification method. It should be noted here that
the sampling frequency used in our computer simulations (published in
[15]) was 1 kHz, which means that d2 represented the frequency range of
250–500 Hz while d3 represented the frequency range of 125–250 Hz.
Since we used a 5-kHz sampling frequency in our experiments, it is
expected that the information about road surface would be similarly
observed in d4 or d5 frequency bands (156.25 Hz < d5 < 312.5 Hz and
312.5 Hz < d4 < 625 Hz). Figure IV-6 20 shows the result of the
experimental back EMF decomposition (related to a braking scenario with
a change in road condition from dry to slippery). The DWT analysis verifies
the result of simulation in that the energy of the signal decreases as the
road surface changes from dry asphalt to slippery [22]. It should, however,
be noted that it is difficult to simulate the condition that the tire-road
friction changes quickly in the ABS test rig and full validation of the
proposed methods for a particular type of hydraulic brake requires
extensive testing on that particular braking system.
58
Figure IV-6. Results of decomposing the experimental armature voltage
by Haar wavelet. Top figure: Dry asphalt. Bottom figure: Road surface changes from
dry asphalt to oily road around the speed of 600 r/min.
b) Accurate Sensorless Wheel-speed Estimation
Calculating the accuracy of sensorless wheel speed estimations requires
that wheel speed be simultaneously measured by a highly precise
method. Those precise measurements can then be used to make a
reasonable calculation of the accuracy of sensorless wheel speed
estimation. Fortunately, the standard ABS test rig for wheel speed
measurement (made by Inteco Ltd ) is equipped with a highly precise
optical encoder with a resolution of up to 2048 CPR (cycles per
revolution). Sensorless wheel speed estimations from brushed DC motor
In-Wheel EV were first compared with the optical encoder
measurements.
59
As discussed in [33], the DC motor back EMF signal has to be pre-
processed before we can compare this signal with the encoder signal. The
back EMF signal amplitude should be adjusted before it can be compared
with the encoder signal. To perform the scaling, the original back EMF
signal was multiplied by a multiplication factor derived according to the
following equation:
(4.5)
In addition, to synchronizing the back EMF and encoder signals, the back
EMF signal should be shifted. The shift amount can be calculated by
finding the time difference between the first local minimums of the two
signals during activation of ABS.
Figure IV-7. Comparison between the back EMF signal and the encoder signal
for dry road and initial speed of 2300 rpm.
100 200 300 400 500 600 700 800 9000
500
1000
1500
2000
Encoder Signal
Sample Point
Wheel S
peed (
RP
M)
100 200 300 400 500 600 700 800 9000
500
1000
1500
2000
Back EMF Signal
Sample Point
Wheel S
peed (
RP
M)
60
Figure IV-8. Comparison between the back EMF signal and the encoder signal (during
ABS activation) for dry road and initial speed of 1000 rpm.
The comparison of the encoder signal and the back EMF signal, depicted in
Figure IV-7, shows that it is possible to calculate the wheel speed from the
back EMF signal of the motor. In fact, our measurements show that there
is a high correlation between the back EMF and the encoder signals. Our
analysis also shows that the back EMF signal follows the same low-
frequency patterns as the encoder signal, particularly at higher wheel
speeds. At lower speeds the correlation is substantially diminished.
However, the ABS is activated only at higher speeds; lower-speed
inaccuracies are not important in this application.
20 40 60 80 100 120 140 160 180 200 2200
500
1000
Encoder Signal
Sample Point
Wheel S
peed (
RP
M)
20 40 60 80 100 120 140 160 180 200 220
0
200
400
600
800
Back EMF Signal
Sample Point
Wheel S
peed (
RP
M)
61
Figure IV-9. Comparison between the back EMF signal and the encoder signal (during
ABS activation) for oily road and initial speed of 1000 rpm.
The above experiments were repeated for different road conditions and
different initial speeds for the start of the braking phase. Figure IV-7
shows a comparison between the back EMF signal and the encoder signal
for dry road at the initial speed of 2300 rpm (high-speed panic braking
scenario). In this figure, the wheel speed is shown for the whole
experiment (i.e. including before the activation of ABS). Figure IV-8 shows
a comparison between the back EMF signal and the encoder signal for the
dry road at the initial speed of 1000 rpm (typical panic-braking scenario).
For the sake of clarity, the wheel speed in this figure is shown only during
the activation of ABS. It shows the back EMF signal still highly correlated
with the encoder signal despite having a different initial speed for the
start of the braking phase.
50 100 150 200 250 300 350 400 4500
500
1000
Encoder Signal
Sample Point
Wheel S
peed (
RP
M)
50 100 150 200 250 300 350 400 450
0
500
1000
Back EMF Signal
Sample Point
Wheel S
peed (
RP
M)
62
Figure IV-10. Deviation of the back EMF signal from the reference signal (encoder
signal) for the dry road condition and an initial speed of 2300 rpm.
In order to compare the back EMF signal with the encoder signal on a
different road condition, the surface of the lower wheel (representing the
road surface) in the ABS test rig was covered with oil, which reduced the
friction coefficient of the road and represented braking on a slippery road.
Figure IV-9 shows a comparison between the back EMF signal and the
encoder signal for oily road at the initial speed of 1000 rpm. This figure
shows that both signals follow the same general pattern. These
experiments show that the back EMF signal patterns are in line with wheel
speed in different road conditions. Although the general trend (low-
frequency component) of the back EMF signal and the encoder signal is
largely similar for different road conditions and for different initial speeds,
data in Figure IV-7 to Figure IV-9 show that the back EMF measurements
are not as smooth as the encoder signal. In other words, there is a small
noise-like discrepancy between the two signals. In order to quantify this
discrepancy, we subtract the back EMF signal from the encoder signal to
0 100 200 300 400 500 600 700 800 900
-600
-400
-200
0
200
400
600
Am
plitu
de o
f E
rror
(RP
M)
Sample Point
Deviation of the back EMF signal from the reference signal (encoder signal)
During
Activation of ABS
Prior to
Activation of ABS
63
produce the error of the back EMF measurements. The deviation of the
back EMF signal from the reference signal (encoder signal) for the dry road
condition and an initial speed of 2300 rpm is shown in Figure IV-10. The
figure shows that the deviation from the reference signal (generated by
the optical encoder) is higher during activation of ABS. Furthermore, the
figure shows that the amplitudes of the error signal during activation of
ABS are higher compared with the part prior to activation of the ABS. The
study of the noise properties of the back EMF before activation of the ABS
is outside the scope of this thesis. However, it is important to note that
the activation of ABS magnifies the effect of noise, which underpins the
need for the dedicated de-noising algorithm based on WP for reliable ABS
operation.
At this point, we introduce an error index for quantifying the amount of
error associated with the back EMF measurements. The error index is
defined as the average of the absolute values of the error. In other word,
the error index is defined according to the following equation:
∑ |R |
(4.6)
where is the number of sample points in the back EMF signal during
activation of ABS. R and represent the measured value for the ith
sample point in the reference (encoder) signal and the back EMF signals,
respectively. Firstly, we calculate this error index for the original back EMF
signal (the back EMF signals without being de-noised). Then, we calculate
the same index for the de-noised back EMF signals by the wavelet packet
and show that the error index has been decreased (i.e. the wheel speed
64
estimation accuracy has been improved).
(A)
(B)
Figure IV-11. (A) Result of wavelet packet de-noising of the back EMF signal related to the dry road condition with initial speed of 1000 rpm using db10 wavelet (B) Reference signal
(Encoder signal).
The result of de-noising a typical back EMF signal during activation of ABS
is shown in Figure IV-11. The figure demonstrates that the original back
20 40 60 80 100 120 140 160 180 200 220
0
100
200
300
400
500
600
700
800
900
Sample Point
Wheel S
peed (
RP
M)
Original Back EMF
De-noised Back EMF
20 40 60 80 100 120 140 160 180 200 220
100
200
300
400
500
600
700
800
900
Sample Point
Wheel S
peed (
RP
M)
De-noised Back EMF
Reference (Encoder) Signal
65
EMF signal is highly noisy and in certain places the magnitude of noise is
substantial compared with the underlying signal (e.g. around sample
point 60). The proposed de-noising method (based on WP) has been able
to successfully reconstruct the underlying signal. The de-noising
significantly improves the speed estimation leading to optimum
performance of the ABS as noisy wheel speed measurements result in
erroneous slip ratio estimation. Figure IV-11 also shows that the de-noised
signal is significantly smoother than the back EMF signal and closely
resembles the reference signal (the optical encoder signal).
As mentioned in chapter II, the WP method can take advantage of
different wavelet shapes to tune itself to the particular noise profile of a
given application. In this thesis, the WP de-noising of the back EMF signal
was performed with the Haar, DMeyer and Daubechies families (db2 to
db10) of wavelets. The comparative results of the improvements in the
error index using different wavelets for de-noising the back EMF signal are
shown in Figure IV-12. This figure shows that the Haar wavelet is
unsuitable for de-noising: the error index was not improved for the back
EMF signal related to the dry road and initial speed of 1000 rpm.
Moreover, the de-noising by Haar and db3 wavelets resulted in the least
improvement for the back EMF signal related to the dry road and initial
speed of 2300 rpm. On the other hand, db7, db9 and db10 successfully
improved the error indices in all of our experiments. It is interesting to
note that the best de-noising results have been achieved for the slippery
road condition where the probability of the ABS activation is higher [33].
66
Figure IV-12. Improvement of the Error Index for the de-noised back EMF signals by different wavelets.
So far we have shown that the back EMF signal can be effectively de-
noised using the WP de-noising method. However, as discussed in [22],
the experimental results of sensorless wheel speed estimation (using both
noisy and de-noised back EMFs) need to be compared with the
measurements of commercial ABS sensors. To ensure more accuracy in all
cases under all conditions, the same WP de-noising method was used, to
eliminate the effect of noise on the accuracy of the new experimental
results. The modified ABS test rig included a commercial ABS sensor made
by Repco. The rest of this section presents the results of those
experiments and compares them with actual ABS sensor measurements.
A typical result of speed measurements from the DC motor back EMF
during the activation of the ABS is shown in Figure IV-13, where the initial
speed for the start of the braking phase is 1000 rpm.
1 2 3 4 5 6 7 8 9 10 110
5
10
15
20
25
Impro
vem
ent
Of
the E
rror
Index
(%)
Dry, 2300 RPM
Dry, 1000 RPM
Oily, 1000 RPM
Haar db10 db7 db6 db5 db4 db2 dmeydb3db8db9
67
Figure IV-13. Results of speed measurement from back EMF (during ABS activation) for a dry road and an initial speed of 1000 r/min.
Figure IV-14. Results of speed measurement from ABS sensor for a dry road and an initial speed of 1000 rpm.
The ABS sensor measurements corresponding to Figure IV-13 are shown in
Figure IV-14. These were obtained using the standard wheel speed
68
measurement method explained in Chapter II. The computation to process
the ABS sensor output is more complex and time-consuming than that for
processing the back EMF signal. This is because the back EMF, being
linearly related to the wheel speed, requires only normalisation to convert
its signal to speed, whereas the conversion of the ABS sensor output
requires the additional step of frequency calculation that was described
earlier. Moreover, as explained in Chapter II, an additional, robust filter is
required to correct the erroneous measurements at low speeds.
As well as needing less computational complexity, our extensive
experiments showed that the accuracy of speed measurement using the
back EMF signal was higher than, or, at worst, similar to that of ABS
sensor measurements. To quantify and compare those measurement
accuracies, we used two error indices, namely, mean absolute error and
mean square error. The mean absolute error (M AE) is defined as:
∑| |
(4.7)
where is the number of sample points and and represent
the measured value for the ith sample point in the reference (encoder)
signal and the pre-processed measurement (back EMF or ABS) signals. The
second index, which places more weight on larger errors, is the mean
square error (MSE) and is defined as:
∑( )
(4.8)
69
Examples of errors associated with speed measurement using the back
EMF signal (shown in Figure IV-13) and ABS sensor measurements (shown
in Figure IV-14) are plotted in Figure IV-15 and Figure IV-16. These
measurement experiments were repeated several times for different road
conditions and initial wheel speeds at the start of the braking phase. The
results of calculating MAE and MSE error indices for both measurements
in those experiments showed that the accuracy of the back EMF
measurement is always higher than (or, in the worst case, similar to) the
accuracy of the ABS sensor measurements. The calculated error indices
for some of those experiments are shown in Table IV-1. This table shows
that the MSE error index is always higher for the ABS sensor, as is the
MAE index , except for rare situations: One is shown in Table IV-1, where
braking started at the initial speed of 1100 rpm on a very slippery road
surface simulated by pouring oil on the road wheel of our test rig.
Figure IV-15. Error of speed measurement from back EMF for a dry road and an initial speed of 1000 rpm.
70
Figure IV-16. ABS sensor measurement error for a dry road and an initial speed of 1000 rpm.
Table IV-1. Error indices for back EMF and ABS sensor measurements.
The results of previous experiments with actual hardware show that it is
feasible to recover the wheel speed information, up to the accuracy
provided by conventional ABS sensors, using the back EMF signal of an In-
Wheel motor and to develop sensorless ABSs without using conventional
ABS sensors. In the rest of this section, we will show that the back EMF
signal can be exploited to achieve speed measurement more accurately
than with conventional ABS sensor measurement. This will enhance the
71
operation of the ABS for In-Wheel vehicles and improve ABS performance
in challenging braking scenarios and extremely noisy environments.
Figure IV-17. Top figure: Result of WP de-noising of the back EMF signal related to the oily road condition with initial speed of 1100 r/min using db7 wavelet. Bottom
figure: De-noised back EMF versus reference signal (encoder signal).
The result of WP de-noising of the back EMF measurements for braking
with ABS at the initial speed of 1100 rpm on an oily road is shown in
Figure IV-17. The figure compares the de-noised back EMF measurements
with both the unprocessed back EMF (top figure) and the reference
measurement (bottom figure) signals. These results show that the de-
noised back EMF signal is a much better predictor of wheel speed. In fact,
the de-noising method has successfully reconstructed an underlying
smooth signal that is closer to the reference signal. As a result, de-noising
contributes to the elimination of erroneous slip-ratio estimations and
improves the performance of the ABS. In a special case where the error of
72
noisy back EMF measurements turned out to be slightly more than the
error of the conventional ABS sensor (see Table IV-1 for an oily road and
the initial speed of 1100 rpm), the MAE error ratio improved from 0.97 to
4.0728, which shows that the de-noising significantly increased the
accuracy of back EMF measurement [22].
73
B. Intelligent Sensorless ABS for Brushless In-Wheel EVs As discussed in [21], electric motors of In-Wheel electric vehicles (EVs) are
desired to have high efficiency, small size and low maintenance
requirements. Brushless motors possess these characteristics and as a
result, have gradually become the number one choice for almost all In-
Wheel EVs [11, 59, 72-76]. This is despite the fact that the dynamics of
brushless motors is more complicated than the brushed motors and
therefore their back EMF outputs are significantly different. In this section,
we investigate the feasibility of developing an intelligent sensorless
antilock braking system (ABS) for brushless motor driven In-Wheel EVs.
We will show that the back EMF of brushless motors, despite their
complicated patterns compared with brushed DC motors, can be used to
measure the wheel speed with similar or more accuracy than commercial
ABS sensors and sensorless wheel speed measurement using brushed DC
motors. In addition, it will be shown in this section that the braking
process on roads with different surface conditions (such as dry or icy)
results in different features in the BLDC back EMF signal of the In-Wheel
hub and these features can be extracted for road identification purposes
by continuous wavelet transform of the back EMF signal during ABS
activation.
Permanent Magnet Synchronous Motors (PMSM) and Brushless DC
(BLDC) motors are two types of Permanent Magnet Brushless (PMBL)
machines that have the same construction. The required magnetic fields
of both machines are generated by rotors having permanent magnets,
and their armature windings, which are connected to three-phase voltage
74
sources, are located on their stators [77]. The PMSM is fed by a three-
phase sinusoidal voltage whereas the BLDC motor is connected to a DC-
chopped, three-phase voltage source. Although the back EMF shape in
PMSM and BLDC is different when those are driven, the back EMF of both
machines as generators are sinusoidal-shape voltages. In this thesis, we
use the back EMF of the brushless machine in the generator mode and
the focus is on the BLDC machine back EMF in the regenerative mode of
operation henceforth.
A BLDC motor consists of a PMSM, a position sensor (Hall-effect sensors)
and an inverter that electronically implements the commutation for the
motor [78]. An electronically commutated BLDC motor is shown in Figure
IV-18. The Hall-effect sensors that are embedded into the stator of BLDC
provide the rotor position feedback signals to the inverter. The inverter
provides direct current to only two phases of the BLDC at any instant of
time. This process is called electronic commutation [79] and is carried out
during the motor operation mode of the BLDC machine.
Figure IV-18. Electronically commutated BLDC motor.
75
Figure IV-19. Equivalent circuit of BLDC machine (phase A).
The equivalent circuit of phase A of the BLDC machine (shown in Figure
IV-18) is shown in Figure IV-19. Base on this model, the voltage of phase A
can be expressed as:
R
(4.9)
where R and are the equivalent resistance and inductance of the
stator phase winding, is the current flowing in phase and is the
back EMF of this phase.
The back EMF voltage of the phase of a BLDC machine can be expressed as:
(4.10)
where and are the electrical velocity and position of the rotor.
Equation (4.10) shows that the amplitude of the back EMF increases as the
speed increases. This is similar to the relationship between the amplitude
of the ABS sensor output and the wheel rotational speed. The rotor
electrical position ( ) is related to the mechanical position ( ) by:
(4.11)
where is the number of pole pairs of the rotor. Equations (4.10) and
(4.11) show that the back EMF signal completes cycles per each
complete rotation of the BLDC rotor. This is similar to the relationship
between voltage and frequency of the ABS sensor signal shown in
equation (2.4).
76
The term “sensorless” in BLDC motor control literatures is often used to
denote the motor position/speed control methods that do not require a
separate position/speed sensor. A technical review of position and speed
sensorless control methods of BLDC motors is given in [79, 80]. Sensorless
control techniques have also been applied to brushless motor-driven
electric vehicles. For example, a hybrid sliding-mode control method of
brushless DC-motor driven EVs was proposed in [72]. Despite the similar
motivations behind sensorless control and sensorless wheel speed
measurement for ABS (i.e. to eliminate the need for position/speed
sensors), their operations are very different. The BLDC motor's sensorless
control methods exploit the back EMF to estimate 'current commutation
points' in motor operation mode. However, the sensorless ABS is intended
to provide continuous wheel speed estimation during ABS activation when
the BLDC is operating as a generator.
The idea of eliminating the ABS sensor stems from the fact that ABS
sensors used in conventional ABSs have several disadvantages. The ABS
ring, which is the most important component of the ABS sensor, is
relatively bulky and makes dimensions of the sensor larger than
commonly used sensors. As mentioned earlier, several components (such
as propulsion, suspension and braking systems) should be integrated
inside a limited space of the In-Wheel hub. As such, omitting the ABS
sensor saves critical space for a better In-Wheel design. The ABS ring is
also vulnerable to dust and corrosion as well as procedures of wheel-hub
inspection or repair. The ABS sensor output is sensitive to the gap space
between the ABS rings and the permanent magnet of the sensor. This
space is hard to adjust and a slight misalignment of this space influences
77
the sensor’s output. Additional wiring is also required for transmitting the
output signal to the ABS controller. This signal can also be influenced by
external disturbances. In contrast to conventional vehicles, for which the
current generation of ABS sensors is designed, the existence of a powerful
electric motor in hubs of EVs means that a significant source of
disturbance, that can distort the ABS signal and deteriorate its
performance, is unavoidable. ABS speed measurements are also
susceptible to mechanical faults in the wheel-bearing assembly, and
electrical faults such as occurrences of open/short circuit and change of
wire resistance [22].
The sensorless ABS design for In-Wheel EVs is one way to address the
above issues. It should also be noted that the assembly, inspection and
maintenance of In-Wheel hubs are more complex and time-consuming
compared with traditional wheel hubs. The sensorless ABS simplifies the
inspection and maintenance procedures of the In-Wheel EVs and reduces
the final price and on-going maintenance costs of the vehicle [22].
A general structural design of the braking system of an EV with four BLDC
motor In-Wheel hubs is shown in Figure IV-20. The wheel speed
information of each BLDC machine in the In-Wheel hub is transmitted to
the central brake controller located in the Electronic Control Unit (ECU) of
the vehicle via the signal transmission and communication network. The
central brake controller then issues required commands to each local
controller which controls the ABS of that wheel according to this
information.
78
Figure IV-20. General structural design of the braking system of an EV with four independently driven BLDC motor In-Wheel hubs.
Figure IV-21. Comparison between ABS sensor (with 48 teeth) output and BLDC motor (with 8 pole pair) back EMF.
An important step in making the ABS sensor redundant is to find a way to
estimate the wheel speed from other available signals in the In-Wheel
79
hub. We earlier argued that the output voltage of the ABS sensor is similar
to the back EMF voltage of the brushless generators. In addition, the
regenerative braking torque is commonly disengaged during activation of
ABS [17]. This means that during the activation of ABS, all phases
(including phase of the BLDC machine shown in Figure IV-19) are open-
circuited and the current in each phase is zero (e.g. in phase ). As
such, equation (4) for ABS activation can be simplified to:
. (4.12)
The above equation shows that the voltage of phase is equal to the
BLDC back EMF and it would be possible to use the voltage of phase to
estimate the wheel speed.
To show the similarity of the BLDC back EMF and ABS sensor signals, we
conducted an experiment in which the ABS ring of a commercial ABS
sensor was connected to the rotor of a BLDC motor. The output of the ABS
sensor and the back EMF of the connected BLDC machine was recorded
and compared. An output voltage of a commercial ABS sensor (with 48
teeth) while it is connected to the rotor of a BLDC motor (with eight pole
pairs) is shown in Figure IV-21. The figure shows that the magnitude and
the frequency of both signals would generally increase as the wheel speed
increases.
We showed earlier that both the magnitude and the frequency of output
voltages of the ABS sensor and the BLDC machine are directly proportional
to the wheel speed. It was also mentioned that wheel speed
measurement from the output of the ABS sensor is currently based on
calculation of the frequency of this signal (the frequency method). The
80
measurement resolution of ABS sensor is related to the number of teeth
(typically 48) in the ABS ring. Although the same frequency method can
also be applied to the BLDC back EMF voltage to measure the wheel speed
during ABS activation, the BLDC machine integrated in the In-Wheel hub
must have enough pole pairs (at least 24) to provide the same
measurement accuracy and resolution compared as the ABS sensor.
To overcome the above restriction, we propose to use the amplitude of
the BLDC back EMF signal and developed an algorithm (for details see the
experimental results part of this section) that uses this source of
information to estimate the wheel speed during ABS activation. Our
experiments also confirmed that by using the amplitude method, wheel
speed estimation can alleviate the constraint on the number of pole pairs
down to only two pole pairs, where the wheel speed measurement
accuracy is almost comparable to ABS sensor measurements. We also
show that the accuracy of the amplitude method significantly increases for
BLDC machines with more than two pole pairs (which is not possible using
the standard frequency method for BLDC motors).
Although our experimental results showed that the amplitude method
was able to estimate the wheel speed with accuracy comparable to that
of the ABS sensor measurement, the required accuracy for a low number
of pole pairs, e.g. two, could not have been guaranteed. To ensure the
required accuracy, an intelligent sensorless ABS using the wavelet signal
processing method was also developed.
81
The proposed amplitude method is able to provide wheel speed
estimation using the BLDC back EMF during ABS activation. However, the
wheel speed accuracy obtained by using this method deteriorated for
BLDC motors with a very low number of pole pairs, e.g. two. Our
experiments, showed that the wheel speed signal (achieved by the
amplitude method) for a BLDC with two pole pairs has a higher frequency
content than its counterpart signal for a BLDC motor with eight pole pairs.
This high-frequency content contains abrupt changes that degrade the
accuracy of wheel speed estimations for BLDC motors with low number of
pole pairs.
To improve the accuracy of wheel speed estimations for BLDC motors
with low number of pole pairs, we propose to decompose the wheel
speed signal achieved by the amplitude method by using the DWT
decomposition with the structure shown in Figure II-8. The proposed
technique based on DWT identifies the low-frequency component/band
(Approximations) that contains accurate wheel speed estimations. In our
experiments, we observed that the wheel speed estimation error
significantly decreases by using the Approximations of the inaccurate
wheel speed estimation (by the amplitude method) at level seven ( ),
for a BLDC motor with low number of (only two) pole pairs.
The implementation detail of our proposed CWT-based feature extraction
algorithm for road identification is as follows. The first step is to compute
the CWT of the BLDC back EMF signal using the Morlet wavelet. We then
define and use the following energy index to distinguish between different
road conditions:
82
√∑∑
(4.13)
where represents sample points of the BLDC back EMF signal and and
are CWT scales and coefficients, respectively. Our experiments
showed that the road-condition features can be extracted by calculating
the above energy index for BLDC back EMF. Our experiments also showed
that the above energy index for road identification is an appropriate
measure for road identification purposes, because its values are
significantly higher for slippery roads (e.g. icy or oily roads) compared
with roads with higher friction coefficients (e.g. dry asphalt). For this
reason, we use the above energy index for road identification during ABS
activation.
1) Experimental Results and Discussions
a) Sensorless Road Identification
To validate the proposed road identification method, outlined earlier in
this section, we carried out several experiments with two different road
conditions (slippery road and dry road) to produce real BLDC back EMF
signals during ABS activation. Since friction coefficients of a road with oily
surface and slippery roads (e.g. icy road) are both significantly lower than
dry road condition, we covered the lower wheel of the ABS test rig
(representing the road surface) with oil in our experiments to create
slippery road condition. The back EMF signals (related to dry and slippery
road condition) were captured and analysed with different continuous
wavelets. It is important to note that the choice of a suitable wavelet
83
shape is important in road identification using equation (4.13) and our
experiments showed that the Morlet wavelet is the most suitable wavelet
for this purpose. Figure IV-22 shows the calculated energy index (defined
in equation (4.13)) in different experiments (with different road conditions
and different initial speed for the start of the braking phase), using the
Morlet wavelet. The results show that the proposed energy index is
sensitive to road conditions as its value significantly increases for slippery
road compared with dry road and can be used for road identification
purposes.
Figure IV-22. Energy index for different road conditions and initial speeds.
b) Sensorless Wheel-speed Estimation
The reference wheel speed provided by the optical encoder before and
after ABS activation in an experiment where the ABS is activated at around
900 RPM, and its corresponding ABS sensor output are shown in Figure
IV-23 and Figure IV-24. The ABS sensor measurements using the ABS
sensor output (shown in Figure IV-24) is depicted in Figure IV-25. As it was
84
mentioned before, we refer to the above wheel speed as ABS sensor
measurements in this thesis.
Figure IV-23. Typical reference wheel speed measurements by optical encoder during a complete experiment.
Figure IV-24. Unprocessed output of ABS sensor.
85
Figure IV-25. Speed measurement from ABS sensor using the standard frequency method (ABS sensor measurements).
To compare the accuracy of different wheel speed measurement
methods, we calculated the Mean Absolute Error (MAE) and the Root
Mean Square Error (RMSE) indices for measurement results of those
techniques. Mean absolute error (MAE) is defined as:
∑| R |
(4.14)
where is the number of sample points and and R denote the
measured values for th sample point provided by the chosen technique
and reference measurement. The other error index, Root Mean Square
Error (RMSE), which emphasises the importance of larger errors, is defined
in Equation (12).
R √∑( R )
(4.15)
For comparison purpose, the ABS sensor measurements during ABS
86
activation versus reference measurements are shown in Figure IV-26. The
errors associated with the above ABS sensor measurements are shown in
Figure IV-27 and the corresponding MAE and RMSE values during ABS
activation are calculated to be 70.6 and 127.2, respectively.
Figure IV-26. Comparison between ABS sensor measurements and reference (optical encoder) measurements during ABS activation.
Figure IV-27. The errors of ABS sensor measurements.
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The Amplitude method
The back EMF of a BLDC machine (with 8 pole pairs) corresponding to
Figure IV-24 is shown in Figure IV-28.
Figure IV-28. BLDC Back EMF (with 8 pole pairs) corresponding to Figure IV-24.
In order to show that the wheel speed can be estimated from the
amplitude of the BLDC back EMF (the amplitude method mentioned
earlier), the absolute values of the amplitudes of the back EMF signal
shown in Figure IV-28 are likewise plotted in Figure IV-29. This figure
shows that there is a high correlation between the absolute values of the
BLDC back EMF signal and reference measurements while the signal
shown in the bottom figure (hereafter referred to as the amplitude signal)
has a higher frequency content compared with the optical encoder signal
shown in the top figure.
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Figure IV-29. Top figure: Reference (optical encoder) measurements. Bottom figure: Absolute values of the back EMF signal (amplitude signal) shown in Figure IV-28.
As we mentioned earlier, we propose to use local maxima of the
amplitude signal to generate a smoother signal with a lower frequency
content (similar to the reference wheel speed signal). A local maximum is
defined as a data sample of the amplitude signal that is larger than its
immediate neighbours’ values. The combination of peak detection and
extrapolation algorithms was then used to reconstruct the wheel speed
signal. The result of applying the peak-detection algorithm on the
amplitude signal shown in Figure IV-29 is shown in Figure IV-30. Wheel
speed estimation using the amplitude method consists of applying the
peak-detection algorithm on the amplitude signal (this step provides a
signal similar to the reference signal) and converting the results to rpm to
be comparable with the reference measurements. We call the final signal
generated by applying the amplitude method the 'peak signal'. The errors
associated with the reconstructed wheel speed signal are plotted in Figure
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IV-31. The MAE and RMSE values for the wheel speed estimation using the
amplitude method (shown in Figure IV-30) are 29.5 and 57.5, respectively.
These results show that the MAE and RMSE of the amplitude method are
58.2 and 54.8 per cent respectively lower than their corresponding values
for the ABS sensor measurements. Results of calculating the MAE and
RMSE values for numerous experiments (with different road conditions
and initial speed for the start of the braking phase) showed that the
accuracy of wheel speed estimation using the amplitude method was
higher than the accuracy of ABS sensor measurements.
Figure IV-30. Peak signal (wheel speed estimations using the amplitude method) corresponding to the amplitude signal shown in Figure IV-29.
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Figure IV-31. The errors associated with the wheel speed estimations shown in Figure IV-30.
The DWT method
To ensure that a more accurate wheel speed estimation compared with
those produced by the commercial ABS sensor, was always achieved
(particularly for BLDC motors with a low number of pole pairs), we used a
more sophisticated approach based on the DWT method described earlier
in this chapter. Figure IV-32 shows that the DWT method successfully
extracts the underlying smooth wheel speed signal from the speed
measurements by the amplitude method for a BLDC with only two pole
pairs.
It is important to choose a suitable wavelet shape when using this
method. To this end, we investigated different wavelet shapes (Haar, db2-
db10, sym2-sym8, coif1-5, bior1.1-bior6.8, rbio1.1-rbio6.8 and DMeyer
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wavelets). the DMeyer wavelet extracted the most similar underlying
wheel speed signal, as shown in Figure IV-32.
The results of calculating MAE and RMSE values for the wheel speed
estimation (in Figure IV-32) by using the DWT method showed that the
method’s accuracy was significantly higher compared with that of the ABS
sensor. MAE and RMSE values decreased 34 and 48 per cent, respectively.
Figure IV-32. Wheel-speed estimation from EMF of a BLDC with only two pole pairs using the DWT method.
Finally, to compare the sensorless wheel speed estimation for BLDC with
that of DC motors during ABS activation, a new coupling system was
designed so that the ABS sensor ring, the rotor of the BLDC motor and the
DC-motor armature would all rotate with the upper wheel (vehicle wheel)
in the modified ABS test rig. Wheel-speed estimation from DC motor back
EMF corresponding to data from Figure IV-23 to Figure IV-31 and the
associated errors are shown in Figure IV-33 and Figure IV-34, respectively.
The RMSE values were approximately the same for both brushed and
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brushless motors. However, the MAE value was calculated to be 7 per cent
lower compared with that of BLDC motor back EMF speed measurement,
which shows that wheel speed estimation for the BLDC with eight pole
pairs (using the amplitude method) was more accurate compared with DC
motor back EMF wheel speed measurement.
Figure IV-33. DC motor back EMF speed measurement.
Figure IV-34. DC motor back EMF error.
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As shown in [22], the back EMF in a DC motor is linearly related to its
armature speed, and this relationship simplifies sensorless wheel speed
estimation for ABS for DC-driven In-wheel EVs. In practice, however, the
back EMF noise produced by brushes in the DC machine degrades the
wheel speed estimation accuracy. On the contrary, the BLDC motor does
not have mechanical brushes and as such, BLDC back EMF is less noisy
compared to that of DC motor. For this reason, the proposed amplitude
method for sensorless wheel speed estimation of BLDC motors was
expected to produce better accuracy compared with DC motors.
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C. Summary of Chapter IV In this chapter, an intelligent sensorless ABS was proposed, developed,
and tested for brushed and brushless In-Wheel EVs. The proposed system
simplifies the In-Wheel E ’s design and significantly reduces final and on-
going maintenance costs by eliminating the need to install separate ABS
sensors at each corner of the vehicle. The proposed system exploits the
information carried by the back electromotive force (EMF) of the electric
machines of the In-Wheel vehicle to simultaneously obtain accurate
wheel speed estimation at each wheel and conduct road identification.
The proposed sensorless ABS was realised and fully tested for brushed DC
motors that have been outstanding in electric vehicle (EV) propulsion
systems due to their mature technology, simple controls and affordable
price. The experimental results showed that the sensorless ABS can
adequately replace the conventional ABS sensor in brushed In-Wheel EVs
and significantly improve the performance of the ABS. However, brushless
technology is currently the dominant design choice for In-Wheel EVs. We
showed that the back EMF signal of the brushless In-Wheel hub can also
be exploited to realise the intelligent sensorless ABS. We conducted a
number of experiments with commercial ABS hardware, actual motors on
different road conditions, and showed that the proposed wheel speed
estimation for BLDC In-Wheel is more accurate compared with
commercial ABS sensors, and hence the need for a separate sensor can be
eliminated.
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Chapter V
Sensor-fusion-based ABS
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V. SENSOR-FUSION-BASED ABS In this chapter, the Sensor-fusion-based wheel speed measurement
system is proposed and developed for ABS of brushed and brushless
motor In-Wheel EVs. The data fusion approach, introduced in chapter II, is
used in this chapter to improve the accuracy, robustness and reliability of
the wheel speed measurement system through efficient use of redundant
information available at each wheel of In-Wheel EVs. It should be
mentioned that providing redundancy for safety critical systems (such as
ABS) via integrating redundant sources of information is a common and
important approach to develop fault tolerant systems [23, 42]. As we
show in later sections, the proposed Sensor-fusion-based method not only
increases the accuracy of wheel speed measurement (which is vital for
optimum performance of the ABS), but also provides redundancy for and
increases the robustness, reliability and fault-tolerance of the wheel speed
measurement system for In-Wheel EVs.
As discussed in [23], despite the fact that the regenerative braking torque
is usually eliminated during activation of ABS, the electric machine in each
wheel would continue to interact with the road. The back EMF voltage of
the electric machine can effectively provide valuable information for
improving the ABS performance of In-Wheel EVs. Simulation and
experimental results in the previous chapter showed that the wheel speed
can be estimated from the back EMF of In-Wheel EVs. In this chapter, low
level (signal level) data fusion algorithms are used to fuse real
experimental data from back EMF of the In-Wheel electric motor with
speed measurement signals extracted from an actual ABS sensor.
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Figure V-1. General structure of a braking system for an In-Wheel EV with four independently driven motors and ABS sensors.
As it was mentioned earlier, an electric machine is integrated into each
wheel hub of In-Wheel EVs. Each wheel hub is also equipped with a
conventional ABS sensor to realize a four-channel ABS (which provides
maximum possible safety in emergency and panic braking situations). The
general architecture of an In-Wheel braking system with four
independently driven motors which also includes an ABS sensor at each
wheel is shown in Figure V-1. Slip ratio control in ABS requires the
measurement of the slip ratio values at each wheel of the vehicle, because
the road condition for each wheel can be different. The proposed wheel
speed measurement system for the above In-Wheel electric vehicle
architecture (shown in Figure V-1) makes use of synergistic combination of
Right
Rear
In-Wheel
Motor
Right
Front
In-Wheel
Motor
Central Brake
Controller
embedded in
ECU
Local
Controller
Signal
Transmission
Signal
Transmission
Driver’s
Braking
Command
Communication
Network
Left
Rear
In-Wheel
Motor
Left
Front
In-Wheel
Motor
Local
Controller
Local
Controller
Signal
Transmission
Signal
Transmission
Local
Controller
ABS
Sensor
ABS
Sensor
ABS
Sensor
ABS
Sensor
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all possible sources of wheel speed information available in the ABS
sensor and motor integrated at each wheel.
Two sources of wheel speed information reside in the ABS sensor output
and those information will be fused with wheel speed information from
the back EMF output of the In-Wheel motor during ABS activation in this
chapter. The first source output is called the “Frequency Signal” and is the
standard wheel speed measurement signal calculated from the frequency
of ABS sensor output signal (described in chapter II). The second source of
information is speed measurements provided by amplitudes of the ABS
(using the amplitude method explained in chapter IV) sensor signal and is
called the “Amplitude Signal”. Although wheel speed estimation from the
amplitude of the ABS sensor signal is known to be noise sensitive and is
not used in practice, as we outline later, our experiments showed that the
inclusion of this information in our calculations increases the overall
accuracy of the wheel speed estimation.
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A. Sensor-fusion-based ABS for Brushed In-Wheel EVs The wheel speed information carried by the back EMF of brushed DC
motor In-Wheel EVs during ABS activation can be effectively fused with
wheel speed information from the ABS sensor. As discussed in [23], it is
common to disengage the regenerative braking torque during ABS
activation. This means that the armature circuit of a brushed DC motor is
an open circuit while ABS is activated and the armature voltage is the
same as the back EMF during ABS activation. The back EMF of brushed DC
motor is linearly related to the wheel speed. By measuring the armature
voltage, speed information for our proposed Sensor-fusion-based wheel
speed estimation strategy is captured.
To improve robustness and accuracy of wheel speed estimation, three
signals (Frequency, Amplitude and DC motor back EMF signals) were
combined using different methods of signal level fusion. Experimental
results in the next section show that Ordered Weighted Averaging (OWA)
is the most appropriate method for brushed DC motors.
1) Experimental Results and Discussions Typical wheel speed measurements from the optical encoder (the
reference measurement) before and after activation of ABS (during a
complete cycle that includes the acceleration and deceleration phases) are
shown in Figure V-2. The back EMF signal shown in the bottom graph
closely resembles the reference measurements shown in the top graph.
The high correlation between the two signals shows that wheel speed can
be estimated from the back EMF signal.
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Figure V-2. Top figure: Typical wheel speed measurements (reference measurements) from the optical encoder connected to the upper wheel. Bottom:
The back EMF signal corresponding to the top figure.
Figure V-3. The output of the ABS sensor corresponding to Figure V-2.
The ABS sensor output signal corresponding to signals depicted in Figure
V-2 is shown in Figure V-3. As mentioned earlier, the frequency and
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amplitude of this signal can be used to reconstruct the wheel speed signal.
The “Frequency method” (standard method of speed measurement from
ABS sensor output signal) was explained in chapter II. In order to show
that the speed can also be estimated from the amplitudes of the ABS
output signal, the absolute values of the ABS output signal are likewise
plotted in Figure V-4. This figure shows that there is a correlation between
the absolute values of the ABS output and actual speeds measured by the
optical encoder (reference measurements). Those figures show that the
peaks of the absolute values can be used to reconstruct the speed signal.
In our implementation, a noisy estimation of wheel speed was obtained
using a peak detection algorithm. We call this source of information the
“Amplitude Signal”. Results of reconstructed signals from the two
different mentioned methods are shown in Figure V-5 and Figure V-6.
Figure V-4. Top figure: Measurements (reference measurements) from the optical encoder connected to the upper wheel. Bottom figure: Absolute values of the ABS
sensor output shown in Figure V-3.
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Figure V-5. Result of wheel speed measurement by the Frequency Method.
Figure V-6. Result of wheel speed measurement by the Amplitude Method.
In order to calculate and compare errors associated with back EMF,
Frequency and Amplitude as well as different fused signals, the Mean
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Absolute Error (MAE) index was used. In order to compare the results of
fusion, the MAE index of different fusion methods are compared with the
MAE index of the conventional method (Frequency method) of calculating
the speed using the commercial ABS sensor output.
Having the three sources of information (Frequency, Amplitude and
back EMF), we can generate four different estimates of the underlying
values using the proposed fusion methods. Results of all possible fusion
combinations of two sources are given in Table V-1. The best results were
achieved by fusion of back EMF and Amplitude signals. The table shows
that the fusion of any two-sensor combinations of the three available
signals always decreases the error index.
Table V-1. Improvement of the MAE index for two-sensor fusion.
Fusion Freq- Amp Freq- EMF EMF-Amp
MAE Improvement 27% 32% 38%
Table V-2 summarizes improvements of the MAE error index associated
with different methods of fusion for three-sensor fusion (Frequency,
Amplitude and back EMF). In this table, the MAE index has been
calculated for all sample points in the fused signal during activation of
ABS. The associated OWA fusion weights are also given in Table V-2 and
the α parameter in all methods is calculated to be 0.55. While minimum
and maximum operators both degraded the results in several cases, the
dispersion based OWA method always improved the error by up to 41
percent. The Median method also improved the MAE index by up to 46
percent. The mean, optimistic and pessimistic exponential OWA also
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improved the MAE index by up to 40 per cent. Comparison of Table V-1
and Table V-2 shows that the fusion of three sensors always results in
better MAE index compared to two-sensor method. The experimental
results showed that the inclusion of the back EMF signal in the fusion
process increased the accuracy of wheel speed measurement significantly.
Table V-2. . Improvement of the MAE index for three-sensor fusion.
Fusion Method Improvement of the MAE Index
Min -14%
Max -5%
Mean 40%
Optimistic OWA 39% w1: 0.55, w2: 0.2475, w3: 0.2025
Pessimistic OWA 39% w1: 0.3025, w2: 0.2475, w3: 0.45
Dispersion OWA 41% w1: 0.285, w2:0.33, w3:0.385
Median 46%
Figure V-7. Fused signal by the OWA (dispersion) method.
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Figure V-8. Fused signal by the Median method.
Results of fusing the three signals by the OWA (dispersion) and Median
method are shown in Figure V-7 and Figure V-8. Comparison of these two
figures shows that the Median method is more successful at lower wheel
speed estimations. However, the ABS is normally de-activated at low
speeds and in this application, lower speed inaccuracies are not
important. As such, we calculated the MAE index for the fused signals
once more by excluding measurements at lower speeds. The calculated
MAE index for this part of fused signals showed that the OWA fusion
method outperforms the Median method by up to 5 per cent.
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B. Sensor-fusion-based ABS for Brushless In-Wheel EVs It was shown in chapter IV that the general form of BLDC back EMF and
ABS sensor outputs are identical. As such, the amplitude and frequency
methods can be used to estimate wheel speed from BLDC Back EMF as
well as ABS sensor output. Schematic diagram of the Sensor-fusion-based
wheel speed measurement system for a BLDC In-Wheel hub is shown in
Figure V-9. The figure shows that the amplitude and frequency methods
can provide up to eight different wheel speed estimations (referred to as
amplitude and frequency signals, respectively) that can be used for the
fusion purpose. Two fusion solutions are investigated in this section. The
first solution uses only the four amplitude signals and the second solution
makes use of all eight amplitude and frequency signals.
Figure V-9. A Schematic diagram of the Sensor-fusion-based wheel speed measurement system for a BLDC motor In-Wheel hub.
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Our experiments showed that the accuracy of the amplitude method was
higher than the frequency method. As such, we first fused four amplitude
signals (three speed signals calculated from the three phases of BLDC and
another signal from the ABS sensor output). The advantage of this solution
is its lesser computational load due to use of only four amplitude signals.
Our experiments also showed that the accuracy of the first solution was
significantly (up to 70 per cent) higher than the accuracy of ABS sensor
measurements alone while fusing all eight signals (the second solution)
improved accuracy by up to 76 per cent.
In addition to the increased wheel speed accuracy, which would
significantly improve the performance of the ABS and decrease the vehicle
stopping distance, our proposed Sensor-fusion-based ABS also improves
the reliability and robustness of the ABS for In-Wheel EVs. Although
conventional vehicles have only one ABS sensor at each wheel and their
ABS sensor output voltages are susceptible to system faults and
disturbances, the chance of those faults occurring is much smaller than
EVs. An electric In-Wheel hub includes several powerful electromagnetic
components (operating at high voltages) that generate significant amount
of electrical disturbances. This means that robustness to externally
induced faults are more important for the ABS of an In-Wheel system. To
examine the effect of those disturbances, we considered the extreme case
of a short circuit fault in the ABS sensor when the wheel speed feedback is
completely cut off.
If the ABS sensor of a wheel of a conventional vehicle were short circuited,
the ABS would not work at that wheel because there is no alternative
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source of wheel speed information. However, we show that a Sensor-
fusion-based ABS for In-Wheel EVs can effectively use the redundant
wheel speed information available in three phases of BLDC to provide
acceptable speed estimation even when the ABS sensor is short circuited.
The two fusion solutions mentioned earlier were implemented, tested and
compared using different signal level fusion methods. In particular, our
experiments showed that the optimistic and pessimistic ordered weighted
averaging (OWA) are appropriate methods for our application.
1) Experimental Results and Discussions The reference wheel speed measured by the test rig’s optical encoder, in
an experiment where the ABS is activated at around 900 RPM, and its
corresponding speed measurements from ABS sensor output using both
the frequency and amplitude methods are shown in Figure V-10.
Figure V-10. Reference (Optical Encoder) Measurements vs Speeds Calculated from ABS Sensor Output using the Amplitude and Frequency Methods.
109
Results of calculating wheel speed from three phases of BLDC (with 8 pole
pairs) using the amplitude method, corresponding to Figure V-10, are
shown in Figure V-11.
Figure V-11. Results of calculating wheel speed from three phases of BLDC using the amplitude method.
Table V-3. Results of calculating the MAE index for different wheel speed measurements.
Method MAE index [RPM]
ABS Sensor (Frequency Method) 64.26
ABS Sensor (Amplitude Method) 56.60
Phase 1 BLDC (Frequency Method) 38.53
Phase 1 BLDC (Amplitude Method) 31.24
Phase 2 BLDC (Frequency Method) 52.10
Phase 2 BLDC (Amplitude Method) 34.24
Phase 3 BLDC (Frequency Method) 40.47
Phase 3 BLDC (Amplitude Method) 33.76
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Table V-3 summarizes results of calculating the MAE index for different
wheel speed measurements. It should be noted that the ABS is normally
deactivated when the vehicle speed is reduced to around 10 km/h. To
include this, the MAE index calculations only incorporate different
measurements up to the point where the peak wheel velocity
(representing vehicle speed) is above 200 RPM (The vehicle speed can be
roughly approximated by connecting the peak values for wheel velocity).
Calculated values for MAE index listed in
Table V-3 show that the accuracy of the amplitude method is always higher
than that of the frequency method (which is commonly used for wheel
speed measurement from conventional ABS sensors). As such, we first
fused results of speed measurement using the amplitude method.
Results of fusing the four amplitude signals (s1 to s4 signals in Figure V-9)
of figures Figure V-10 and Figure V-11 by OOWA, POWA and Median
methods are shown in Figure V-12, Figure V-13, and Figure V-14
respectively. The calculated values for MAE index corresponding to Figure
V-12, Figure V-13, and Figure V-14 can be compared with those of
conventional fusion methods in Figure V-15. This figure shows that the
POWA fusion method has the minimum MAE index value. Our
experimental results carried out on different road conditions (Dry and
slippery roads) with different initial speeds for the start of the braking
phase showed that the POWA fusion method can decrease the MAE value
by up to 70 per cent in comparison with ABS sensor measurements.
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Figure V-12. Fused signal by the OOWA method.
Figure V-13. Fused signal by the POWA method.
112
Figure V-14. Fused signal by the Median method.
Figure V-15. Comparison among different fusion methods, when fusing s1 to s4 signals.
113
While the accuracy of POWA and OOWA was higher than that of the
median method when using only four amplitude signals (s1 to s4 signals in
Figure V-9) the median method accuracy was higher than all other fusion
methods when all eight signals (s1 to s8 signals in Figure V-9), were fused.
Results of fusing all eight signals using the median method are shown in
Figure V-16. Our experimental results on different road conditions (see
above paragraph) showed that the median fusion method can decrease
the MAE value by up to 76 per cent in comparison with the commercial
ABS sensor measurements.
Figure V-16. Fused signal by the Median method (s1 to s8 signals are fused).
To test the robustness of the proposed Sensor-fusion-based ABS, we
conducted an experiment in which the ABS sensor was short circuited.
Table V-4 summarizes the MAE values calculated for OOWA, POWA and
median methods for the above experiments. The MAE values in the table
show that all fusion methods (OOWA, POWA, Median), for the case when
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the ABS sensor was short circuited, could still provide acceptable wheel
speed estimations compared with a commercial ABS sensor alone (with
MAE of around 65 RPM).
Table V-4. MAE values calculated for OOWA, POWA and median methods where ABS sensor is short circuited.
Fusion Method MAE Value [RPM] fusion of s1 to s4
MAE Value [RPM] fusion of s1 to s8
OOWA 39.96 112.54
POWA 30.27 71.58
Median 37.85 19.59
Finally, we compared the accuracy of Sensor-fusion-based wheel speed
estimation for BLDC In-Wheel hub with that of a DC motor. Experimental
results showed that the MAE value for BLDC Sensor-fusion-based ABS is
less than that of DC motors. DC motors use brushes for electrical
commutation which degrade their back EMF quality. Also, the Sensor-
fusion-based ABS for Brushless DC (BLDC) motors introduced in this paper
made use of wheel speed information available in three phases of the
electric machine. As brushed DC motors have only one phase and
therefore only one back EMF can be fused with ABS sensor output (see
[23]). For these reasons, the fusion based ABS for BLDC driven In-Wheel
EVs met our expectations and produced better accuracy compared with
brushed DC motors.
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C. Summary of Chapter V In this chapter, a Sensor-fusion-based ABS was proposed, developed and
tested for brushed and brushless In-Wheel EVs. The proposed method
fuses the available information in the motor back EMF with ABS sensor
measurements to increase the accuracy, robustness and reliability of the
wheel speed measurement system of ABS for In-Wheel EVs.
The experimental results showed that the accuracy of the proposed
Sensor-fusion-based method for brushed DC motor was improved by up to
46 per cent in comparison with a commercial ABS sensor. Experimental
results also showed that the accuracy of the fusion based method for
BLDC motor, which uses four signals for the fusion purpose, is up to 70
per cent higher compared to commercial ABS sensor measurements used
in conventional vehicles. Also, by increasing the number of available
signals from four to eight, the wheel speed accuracy can be improved by
up to 76 per cent. In addition to the improved accuracy, which significantly
improves the performance of ABS and decreases the stopping distance,
the method is robust to ABS sensor failures such as ABS sensor short
circuit fault. Finally, Experimental results showed that the accuracy and
robustness of the Sensor-fusion-based ABS for BLDC motors was better
compared to brushed DC motor In-Wheel EVs. The improvements are due
to the availability of speed information in three phases of BLDC and higher
quality (less noise) of the brushless back EMF delivered by a BLDC motor.
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Chapter VI
Conclusion and Future Work
117
VI. CONCLUSION AND FUTURE WORK Electric vehicles and In-Wheel technology are on the threshold of
exponential growth. The availability of a separate electric machine at each
corner of In-Wheel EVs opens the door for the development of innovative
solutions for precise motion control of EVs. Separate electric machines
used for the propulsion of the In-Wheel EV also provide regenerative
power during normal braking to extend the vehicle range. However,
regenerative brakes are currently combined with frictional ones to satisfy
stringent safety requirements for passenger vehicle designs but are
disengaged when the ABS is activated because energy saving is not a
priority in an emergency braking situation.
Although the regenerative braking torque of the In-Wheel motor is not
utilised during the braking phase, the motor back EMF can provide
valuable information for the control of the conventional ABS. The
assumption underpinning the proposed approaches in this thesis was that
the regenerative back EMF could be used to simplify and improve the
wheel speed measurement part of the ABS for In-Wheel EVs.
The In-wheel design requires several components to be installed in a
limited physical space of the wheel hub, which complicates and adds to
the cost of both the design and maintenance. The proposed sensorless
ABS exploited the back EMF to eliminate the need to install a separate
conventional ABS sensor for the In-Wheel design. The proposed
sensorless system was first developed for brushed DC propulsion and it
was shown that the wheel speed estimation from the back EMF has
higher accuracy and less computational complexity compared with
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commercial ABS sensors. The system has also been shown to improve the
performance of existing ABS controllers via accurate wheel speed
estimation and road identification. The experimental results showed that
the accuracy of wheel speed estimation from the back EMF of the
brushed In-Wheel electric machine is higher compared with the actual
ABS sensor used in passenger cars. We also developed the sensorless ABS
for brushless motor propulsion and showed that the back EMF signal of
the brushless In-Wheel hub can be exploited to produce accurate wheel
speed estimation and perform road identification, both of which
contribute significantly to the ABS performance. We conducted a number
of experiments with different hardware and road conditions and showed
that the accuracy of the proposed wheel speed estimation for BLDC In-
Wheel is higher compared with commercial ABS sensors, eliminating the
need for a separate sensor. A successful implantation of the proposed
intelligent sensorless ABS is beneficial in terms of mechanical design
simplicity and the cost of wheel hub manufacturing and maintenance.
This research also introduced and developed a Sensor-fusion-based ABS
for brushed and brushless In-Wheel EVs. In the proposed system, the
wheel speed was estimated based on data fusion. The system was
extensively tested using real ABS hardware and actual motors. The
proposed method makes use of the back EMF of In-Wheel motor (in
addition to measurements from an ABS sensor) for wheel speed
estimation during ABS activation. Improved accuracy, reliability and
robustness of the ABS were among the characteristics of our proposed
method. The experimental results of fusing measured back EMF signals
with measurements from an actual ABS sensor showed that the wheel
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speed accuracy and robustness of ABS can be significantly improved using
our proposed Sensor-fusion-based methods. The realisation of the
presented Sensor-fusion-based ABS is beneficial in improving the
performance and robustness of their ABS.
The proposed sensorless ABS and Sensor-fusion-based ABS are two
different designs for In-Wheel EVs that make the antilock braking system
of the vehicle more efficient. Each of the two approaches has their own
advantages. Advantages of implementing the sensorless method are
mainly related to eliminating the need to install a separate ABS sensor at
each corner of the vehicle, whereas the advantages of the Sensor-fusion-
based approach are improvement of the accuracy, reliability and
robustness for the wheel speed measurement system of the ABS.
Intelligent roads and automated highways have recently gained attention
and extensive research is being carried out worldwide in this field. Such
roads are envisaged to realise vehicle-to-vehicle communication and
incorporate several sensor technologies. The Sensor-fusion-based ABS idea
introduced in this thesis can be further improved and developed for such
roads.
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[2] W. Y. Wang, I. H. Li, M. C. Chen, S. F. Su, and S. B. Hsu, "Dynamic slip-ratio estimation and control of antilock braking systems using an observer-based direct adaptive fuzzy-neural controller," IEEE Trans. Ind. Electron., vol. 56, pp. 1746-1756, 2009.
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List of Publications:
Journal Papers:
#1 Dadashnialehi, A., Bab-Hadiashar, A.; Cao, Z.; Kapoor, A., "Intelligent Sensorless ABS for In-Wheel Electric Vehicles", Industrial Electronics, IEEE Transactions on , vol.61, no.4, pp.1957-1969, April 2014
#2 Dadashnialehi, A., Bab-Hadiashar, A.; Cao, Z.; Kapoor, A., "Intelligent Sensorless Antilock Braking System for brushless In-Wheel Electric Vehicles", Industrial Electronics, IEEE Transactions on, in press
#3 Dadashnialehi, A., Bab-Hadiashar, A.; Cao, Z.; Kapoor, A., "Sensor-fusion-based ABS for Brushless In-Wheel
Electric Vehicles", to be submitted
Conference Papers:
#4 Dadashnialehi, A., Zhenwei Cao; Kapoor, A.; Bab-Hadiashar, A., "Intelligent sensorless ABS for regenerative brakes," Electric Vehicle Conference (IEVC), 2012 IEEE International , pp. 1-5, 4-8 March 2012
#5 Dadashnialehi, A., Bab-Hadiashar, A.; Zhenwei Cao; Kapoor, A., "Accurate wheel speed measurement for sensorless ABS in electric vehicle", Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on, pp. 37-42, 24-27 July 2012
#6 Dadashnialehi, A., Bab-Hadiashar, Alireza; Cao, Zhenwei; Kapoor, Ajay, "Enhanced ABS for In-Wheel Electric Vehicles using data fusion", Intelligent Vehicles Symposium (IV), 2013 IEEE, pp. 702-707, 23-26 June 2013
Patent:
#7 'Estimating Wheel Speed for In-wheel Electric Vehicles.', Patent Number: 2014203462, Inventor: Amir Dadashnialehi
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