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

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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

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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. ................................................................................................. 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.

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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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References:

[1] Y. Oniz, E. Kayacan, and O. Kaynak, "A dynamic method to forecast the wheel slip for antilock braking system and its experimental evaluation," IEEE Trans. Syst., Man, and Cybern. B, Cybern., vol. 39, pp. 551-560, Apr. 2009.

[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.

[3] R. Hoseinnezhad and A. Bab-Hadiashar, "Efficient antilock braking by direct maximization of tire-road frictions," IEEE Trans. Ind. Electron., vol. 58, pp. 3593-3600, Aug. 2011.

[4] E. Kayacan, Y. Oniz, and O. Kaynak, "A grey system modeling approach for sliding-mode control of antilock braking system," IEEE Trans. Ind. Electron., vol. 56, pp. 3244-3252, 2009.

[5] A. V. Topalov, Y. Oniz, E. Kayacan, and O. Kaynak, "Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm," Neurocomputing, vol. 74, pp. 1883-1893, 2011.

[6] W. Rattasiri, N. Wickramarachchi, and S. K. Halgamuge, "An optimized anti-lock braking system in the presence of multiple road surface types," International Journal of Adaptive Control and Signal Processing, vol. 21, pp. 477-498, 2007.

[7] S. M. Savaresi, M. Tanelli, and C. Cantoni, "Mixed slip-deceleration control in automotive braking systems," Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 129, pp. 20-31, 2007.

[8] J. Zhang and D. Kong, "The integrated control of regenerative braking system and air braking ABS on electric wheel drive urban bus," in Electric Information and Control Engineering (ICEICE), 2011 International Conference on, 2011, pp. 4622-4625.

[9] C. Song, J. Wang, and L. Jin, "Study on the Composite ABS Control of Vehicles with Four Electric Wheels," Journal of Computers, vol. 6, pp. 618-626, Mar. 2011.

[10] R. de Castro, R. E. Araujo, and D. Freitas, "Wheel Slip Control of EVs Based on Sliding Mode Technique With Conditional Integrators," IEEE Trans. Ind. Electron., vol. 60, pp. 3256-3271, Aug. 2013.

[11] L. Weng-Ching, L. Chun-Liang, H. Ping-Min, and W. Meng-Tzong, "Realization of Anti-Lock Braking Strategy for Electric Scooters," IEEE Trans. Ind. Electron., vol. 61, pp. 2826-2833, 2014.

[12] S. Oleksowicz, K. Burnham, P. Barber, B. Toth-Antal, G. Waite, G. Hardwick, et al., "Investigation of regenerative and Anti-lock Braking interaction," International Journal of Automotive Technology, vol. 14, pp. 641-650, 2013.

[13] L. Sha, Q. Du, and K. W. E. Cheng, "ABS Control of Electric Vehicle on Various Road Conditions," in Control, Automation and Systems Engineering (CASE), 2011 International Conference on, 2011, pp. 1-4.

[14] L. Chun-Liang and Y. Meng-Yao, "Design of anti-lock braking system for electric vehicles via short-circuit braking," in Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on, 2011, pp. 1176-1179.

[15] A. Dadashnialehi, Z. Cao, A. Kapoor, and A. Bab-Hadiashar, "Intelligent sensorless ABS for regenerative brakes," in Electric Vehicle Conference (IEVC), 2012 IEEE International 2012, pp. 1-5.

121

[16] O. Tur, O. Ustun, and R. N. Tuncay, "An Introduction to Regenerative Braking of Electric Vehicles as Anti-Lock Braking System," in Intelligent Vehicles Symposium, 2007 IEEE, 2007, pp. 944-948.

[17] R. C. Kosik, S. O. Pate, and W. F. Sanderson, "Temperature compensated lift-throttle regenerative braking " U.S. Patent 6588860B2, Jul. 8, 2003.

[18] K. Cakir and A. Sabanovic, "In-wheel motor design for electric vehicles," in Advanced Motion Control, 2006. 9th IEEE International Workshop on, 2006, pp. 613-618.

[19] Y. Hori, "Future vehicle driven by electricity and control - Research on four-wheel-motored "UOT Electric March II"," IEEE Transactions on Industrial Electronics, vol. 51, pp. 954-962, 2004.

[20] S. I. Sakai, H. Sado, and Y. Hori, "Motion control in an electric vehicle with four independently driven in-wheel motors," IEEE/ASME Transactions on Mechatronics, vol. 4, pp. 9-16, 1999.

[21] A. Dadashnialehi, A. Bab-Hadiashar, Z. Cao, and A. Kapoor, "Intelligent Sensorless Antilock Braking System for Brushless In-Wheel Electric Vehicles," IEEE Trans. Ind. Electron., in press.

[22] A. Dadashnialehi, A. Bab-Hadiashar, Z. Cao, and A. Kapoor, "Intelligent Sensorless ABS for In-Wheel Electric Vehicles," IEEE Trans. Ind. Electron., vol. 61, pp. 1957-1969, Apr. 2014.

[23] A. Dadashnialehi, A. Bab-Hadiashar, Z. Cao, and A. Kapoor, "Enhanced ABS for In-Wheel Electric Vehicles Using Data Fusion," in Proc. IEEE IV Gold Coast, Australia, 2013.

[24] J. A. Cabrera, A. Ortiz, J. J. Castillo, and A. Simón, "A fuzzy logic control for antilock braking system integrated in the IMMa tire test bench," IEEE Transactions on Vehicular Technology, vol. 54, pp. 1937-1949, 2005.

[25] H. Jing, Z. Liu, and H. Chen, "A switched control strategy for antilock braking system with on/off valves," IEEE Transactions on Vehicular Technology, vol. 60, pp. 1470-1484, 2011.

[26] T. A. Johansen, J. Kalkkuhl, J. Lüdemann, and I. Petersen, "Hybrid control strategies in ABS," in American Control Conference, 2001. Proceedings of the 2001, 2001, pp. 1704-1705.

[27] C. Mi, H. Lin, and Y. Zhang, "Iterative learning control of antilock braking of electric and hybrid vehicles," IEEE Transactions on Vehicular Technology, vol. 54, pp. 486-494, 2005.

[28] Q. Zhang, G. Liu, B. Liu, and X. Xie, "Sensor fusion based estimation technology of vehicle velocity in anti-lock braking system," in Information Acquisition, 2007. ICIA '07. International Conference on 2007, pp. 106-111.

[29] M. Amiri and B. Moaveni, "Vehicle velocity estimation based on data fusion by Kalman filtering for ABS," in Electrical Engineering (ICEE), 2012 20th Iranian Conference on, 2012, pp. 1495-1500.

[30] R. Schwarz, O. Nelles, P. Scheerer, and R. Isermann, "Increasing signal accuracy of automotive wheel-speed sensors by on-line learning," in American Control Conference, 1997. Proceedings of the 1997, 1997, pp. 1131-1135.

[31] W. Hernández, "Improving the response of a wheel speed sensor by using frequency-domain adaptive filtering," IEEE Sensors J., vol. 3, pp. 404-413, 2003.

[32] W. Hernández, "Improving the response of a wheel speed sensor using an adaptive line enhancer," Measurement, vol. 33, pp. 229-240, 2003.

[33] A. Dadashnialehi, A. Bab-Hadiashar, Z. Cao, and A. Kapoor, "Accurate wheel speed measurement for sensorless ABS in electric vehicle," in Proc. IEEE ICVES 2012, 2012, pp. 37-42.

[34] S. Mallat, A Wavelet Tour of Signal Processing, 2009.

122

[35] I. Doubechies, "Ten lectures on wavelet," Philadelphia: CBMS-NSF Series in Applied Math, 1992.

[36] A. Graps, "An introduction to wavelets," Computational Science & Engineering, IEEE, vol. 2, pp. 50-61, 1995.

[37] G. P. Nason, "A little introduction to wavelets," in Applied Statistical Pattern Recognition (Ref. No. 1999/063), IEE Colloquium on, 1999, pp. 1/1-1/6.

[38] C. Gargour, M. Gabrea, V. Ramachandran, and J. M. Lina, "A short introduction to wavelets and their applications," Circuits and Systems Magazine, IEEE, vol. 9, pp. 57-68, 2009.

[39] A. Abidi and R. C. RGonzalez, Data Fusion in Robotics and Machine Intelligence: Academic Press, 1992.

[40] R. C. Luo, Y. Chih-Chen, and S. Kuo-Lan, "Multisensor fusion and integration: approaches, applications, and future research directions," IEEE Sensors J., vol. 2, pp. 107-119, 2002.

[41] R. C. Luo and M. G. Kay, "A tutorial on multisensor integration and fusion," in Industrial Electronics Society, 1990. IECON '90., 16th Annual Conference of IEEE, 1990, pp. 707-722 vol.1.

[42] R. a. B.-H. A. Hoseinnezhad, "Fusion of redundant information in brake-by-wire systems using a fuzzy voter," Journal of Advances in Information Fusion, vol. 1, pp. 52-62, 2006.

[43] S. Saric, A. Bab-Hadiashar, and R. Hoseinnezhad, "Clamp-force estimation for a brake-by-wire system: a sensor-fusion approach," Vehicular Technology, IEEE Transactions on, vol. 57, pp. 778-786, 2008.

[44] S. Koskinen, "Sensor data fusion based estimation of tyre-road friction to enhance collision avoidance," VTT Publication (730), 2010.

[45] A. Dadashnialehi and B. Moshiri, "Online monitoring of transit-time ultrasonic flowmeters based on fusion of optical observation," Measurement: Journal of the International Measurement Confederation, vol. 44, pp. 1028-1037, 2011.

[46] B. Tokhmechi, H. Memarian, H. Noubari, and B. Moshiri, "A novel approach proposed for fractured zone detection using petrophysical logs," Journal of Geophysics and Engineering, vol. 6, p. 365, 2009.

[47] R. HoseinNezhad, B. Moshiri, and M. R. Asharif, "Improved pose estimation for mobile robots by fusion of odometry data and environment map," Journal of Intelligent and Robotic Systems, vol. 36, pp. 89-108, 2003.

[48] R. R. Yager, "ORDERED WEIGHTED AVERAGING AGGREGATION OPERATORS IN MULTICRITERIA DECISIONMAKING," IEEE Transactions on Systems, Man and Cybernetics, vol. 18, pp. 183-190, 1988.

[49] M. Kazemian, B. Moshiri, H. Nikbakht, and C. Lucas, "Protein secondary structure classifiers fusion using OWA," in Biological and Medical Data Analysis, ed: Springer, 2005, pp. 338-345.

[50] Z. Xu and Q.-L. Da, "An overview of operators for aggregating information," International Journal of Intelligent Systems, vol. 18, pp. 953-969, 2003.

[51] M. O'Hagan, "Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic," in Signals, Systems and Computers, 1988. Twenty-Second Asilomar Conference on, 1988, pp. 681-689.

[52] D. Filev and R. R. Yager, "On the issue of obtaining OWA operator weights," Fuzzy Sets and Systems, vol. 94, pp. 157-169, 1998.

[53] "The Laboratory Antilock Braking System Controlled from PC ", ed: Inteco Ltd, Kraków, Poland, 2006.

123

[54] "The Laboratory Antilock Braking System Controlled from PC," ed: Inteco Ltd., Krakow, Poland, 2011.

[55] E. Geobel, M. Fuss, and I. Alfter, "Method and device for detecting a rotational speed, especially the rotational speed of the wheel of a vehicle," US 7248 991 B2.

[56] E. Kayacan, Y. Oniz, and O. Kaynak, "A Grey System Modeling Approach for Sliding-Mode Control of Antilock Braking System," IEEE Transactions on Industrial Electronics, vol. 56, pp. 3244-3252, Aug 2009.

[57] Y. Hori, "Future vehicle driven by electricity and control - Research on four-wheel-motored "UOT Electric March II"," IEEE Trans. Ind. Electron., vol. 51, pp. 954-962, Oct. 2004.

[58] M. A. Çinar and F. E. Kuyumcu, "Design and drives simulation of an in-wheel switched reluctance motor for electric vehicle applications," in Electric Machines & Drives Conference, 2007. IEMDC '07. IEEE International 2007, pp. 50-54.

[59] M. Terashima, T. Ashikaga, T. Mizuno, K. Natori, N. Fujiwara, and M. Yada, "Novel motors and controllers for high-performance electric vehicle with four in-wheel motors," IEEE Trans. Ind. Electron., vol. 44, pp. 28-38, Feb. 1997.

[60] M. Ehsani, Y. Gao, and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, 2nd ed.: Boca Raton, FL, USA: CRC, 2010.

[61] N. Hashemnia and B. Asaei, "Comparative study of using different electric motors in the electric vehicles," in Electrical Machines, 2008. ICEM 2008. 18th International Conference on, 2008.

[62] T. T. Vu, D. T. Lien, T. W. Chun, H. H. Lee, H. G. Kim, and E. C. Nho, "Development of low-cost drive system of a small-size electric vehicles," in Power Electronics and ECCE Asia (ICPE & ECCE), 2011 IEEE 8th International Conference on, 2011, pp. 1946-1949.

[63] A. Muetze and Y. C. Tan, "Modeling and analysis of the technical performance of DC-motor electric bicycle drives based on bicycle road test data," in Electric Machines & Drives Conference, 2007. IEMDC '07. IEEE International, 2007, pp. 1574-1581.

[64] X. Liu, A. Li, Y. Hori, T. Akiba, and R. Shirato, "Optimal traction control for EV utilizing fast torque response of electric motor," in Industrial Electronics Society, 2005. IECON 2005. 31st Annual Conference of IEEE, 2005, pp. 2614-2619.

[65] M. Sri-Jayantha, G. F. Hayhoe, and J. J. Henry, "SCR-CONTROLLED DC-MOTOR MODEL FOR AN ELECTRIC VEHICLE PROPULSION SYSTEM SIMULATION," IEEE Transactions on Industrial Electronics, vol. IE-31, pp. 18-25, 1984.

[66] V. Gupta and A. Deb, "Speed control of brushed DC motor for low cost electric cars," in Electric Vehicle Conference (IEVC), 2012 IEEE International 2012.

[67] M. Alatalo, S. T. Lundmark, and E. A. Grunditz, "Electric machine design for traction applications considering recycling aspects-review and new solution," in IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society, 2011, pp. 1836-1841.

[68] M. Hilairet and F. Auger, "Speed sensorless control of a DC-motor via adaptive filters," IET Electric Power Applications, vol. 1, pp. 601-610, 2007.

[69] G. S. Buja, R. Menis, and M. I. Valla, "Disturbance torque estimation in a sensorless DC drive," IEEE Transactions on Industrial Electronics, vol. 42, pp. 351-357, 1995.

[70] E. Vázquez-Sánchez, J. Gómez-Gil, J. C. Gamazo-Real, and J. F. Díez-Higuera, "A new method for sensorless estimation of the speed and position in brushed DC motors using support vector machines," IEEE Transactions on Industrial Electronics, vol. 59, pp. 1397-1408, 2012.

[71] A. Ordaz-Moreno, R. d. J. Romero-Troncoso, J. A. Vite-Frias, J. R. Rivera-Gillen, and A. Garcia-Perez, "Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation," IEEE Transactions on Industrial Electronics, vol. 55, pp. 2193-2202, 2008.

124

[72] Y. Wang, X. Zhang, X. Yuan, and G. Liu, "Position-sensorless hybrid sliding-mode control of electric vehicles with brushless DC motor," IEEE Trans. Veh. Technol., vol. 60, pp. 421-432, Feb. 2011.

[73] H. Kahveci, H. I. Okumus, and M. Ekici, "An electronic differential system using fuzzy logic speed controlled in-wheel brushless DC motors," in Proc. POWERENG, 2013, pp. 881-885.

[74] R. Vandana and B. G. Fernandes, "Optimal sizing of motor - Battery system for in wheel electric vehicles," in Proc. IEEE IECON 2010, pp. 2510-2515.

[75] M. G. Simoes and P. Vieira, Jr., "A high-torque low-speed multiphase brushless machine-a perspective application for electric vehicles," IEEE Trans. Ind. Electron., vol. 49, pp. 1154-1164, 2002.

[76] K. T. Chau, C. C. Chan, and L. Chunhua, "Overview of Permanent-Magnet Brushless Drives for Electric and Hybrid Electric Vehicles," IEEE Trans. Ind. Electron., vol. 55, pp. 2246-2257, 2008.

[77] X. Zhang and C. Mi, Vehicle Power Management Modeling, Control and Optimization: Springer, 2011.

[78] S. Ogasawara and H. Akagi, "An approach to position sensorless drive for brushless DC motors," IEEE Trans. Ind. Appl., vol. 27, pp. 928-933, 1991.

[79] J. C. Gamazo-Real, E. Vázquez-Sánchez, and J. Gómez-Gil, "Position and speed control of brushless dc motors using sensorless techniques and application trends," Sensors, vol. 10, pp. 6901-6947, 2010.

[80] P. P. Acarnley and J. F. Watson, "Review of position-sensorless operation of brushless permanent-magnet machines," IEEE Trans. Ind. Electron., vol. 53, pp. 352-362, Apr. 2006.

125

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