Intelligent+Anti Lock+Braking+Control+of+Hybrid+Buses

8/12/2019 Intelligent+Anti Lock+Braking+Control+of+Hybrid+Buses

1/9

-

CN 11-5904/U

, , J Automotive Safety and Energy, 2010, Vol. 1 No. 1 4048

Intelligent Anti-lock Braking Control of Hybrid Buses

q ang , n

(School of Mechanical and Power Engineering, Shanghai Jiao Tong University, Shanghai 200240,China)

Abstract: This paper proposes an intelligent anti-lock braking control of hybrid buses through a neuro-fuzzy

controller (NFC) by combining the fuzzy logic algorithm and the artificial neural network. The braking torque

distribution between the integrating starting generator (ISG) and the friction disc brake is addressed through the

proposed NFC. The experimental results show that the braking performance and the braking regeneration can both

be optimized through the NFC for vehicle safety and fuel economy.

Keywords: integrating starting generator (ISG); hybrid bus; fuzzy logic control; artificial neural network; anti-lock

braking; System (ABS)

200240

(NFC)

ISG

(ISC) (ABS)

eceived: 2010-01-26

Supported by the National Basic Research (1973) Program of China (No. 2007CB209707) o w om correspon ence s ou e a resse . -ma : c enz q ang s tu.e u.cn

ntroduction

Anti-lock braking system (ABS) is one of most important

safety system in vehicles. Many theories and design meth-

ods for anti-lock braking systems have been proposed sev-

era teratures or eca es. Researc ers ave cons ere a

ot o contro s trateg es an met o s o ant - oc ra ng

systems, w c ave een emonstrate e ect ve or ABS

system. Georg proposed fuzzy technique for ABS and

Nelson et al. has implemented fuzzy logic based ABS

for electric vehicle. But as a rule based on control strategy,

t nee s arge amount o uzzy ru es to support t e ca -

cu at on. W t comp cate searc an n erence o ru es,

FLC ta es muc ca cu at ng t me an t us as cu ty

to realize a real time control . While through optimizing

of structure and algorithm, Fuzzy logic based control for

ABS can meet the requirement of real-time control and is

still attractive for researches in recent years .

Fuzzy logic controller (FLC) has already been applied

n the control of vehicle over fifteen years . While

the hybrid dynamic systems are usually difficult to be

contro e ecause o non- near an t me vary ng, an

eac su system a so as ts own contro er. Hence many

c a enges ex st or es gn ng a ve c e system contro -er for a parallel hybrid electric vehicle (PHEV). Given

this complexity, Fuzzy Logic Control is very suitable for

hybrid vehicle control-

. Although with complicated

searc an n erence o ru es, FLC ta es muc ca cu at-

ng t me an t us as cu ty to rea ze a rea t me

contro , W e t roug opt m z ng o structure an

algorithm, Fuzzy logic based control for ABS can still

eet the requirement of real-time control . With the


2/9

CHEN Ziqiang, et al.: nte gent nt - oc ra ng ontro o y r uses

development of ANN theory, the combination of FLC

and ANN attracts more and more researchersattention

an seems to e a v a e met o or y r ve c e app -

cat ons. Neuro- uzzy contro er NFC s ust suc a y-

brid system and many researches-

have already done

some studies in their papers.

In this paper, a neuro-fuzzy algorithm applied in

the regenerative braking of hybrid bus is established

t roug t e com nat on o t e uzzy a gor t m an

t e ac -propagat on BP networ . It rea zes uzzy

algorithm through neural network and the fuzzy rules

are interspersed impliedly in the network through

t e tra n ng o ANN. T e ra ng torque str ut on

etween ISG an sc ra e as een e te rm ne y

the algorithm through inputs of bus velocity, wheel

speed, and travel of brake pedal and its variance ratio

t o t e NFC. Moreover, t e neu ro - u zz y a gor t m

s a so ntegrate w t t e contro pr nc p e o ant -

ock braking system, and the braking torque of bus is

adjusted continuously through the corrected values of

ISG torque an ra ng o pressure to prevent w ee s

from locking, therefore the ABS functionality for

hybrid electric buses is also delivered by the NFC.

In t e rest o t s paper, sect on 1 ntro uces t e y r

dynamic system and the braking system of hybrid

electric buses. Section 2 describes the model of the

str ut on o ra ng torque o t e system. S mu at ons presente n sect on 3. Exper ments or va at ng t e

ABS functionality and regenerative braking are given in

the section 4, followed by conclusions in section 5.

1 ybrid Dynamic System and BrakingSystem

1.1 Structure of hybrid dynamic system

T e y r ynam c system n t e paper s ma n y

cons ste o ese eng ne, Integrate Starte Generator

ISG , transm ss on ox, power attery pac s an attery

control module, DC motor control module (DMCM),

automatic disconnection module (ADM), braking system,

and hybrid control unit (HCU), as shown in fig. 1.

HCU is the central control unit of the system being

responsible for the signal collecting, signal processing,

and the control, supervision and harmonization of the sub

contro mo u es. T e contro er area networ CAN s

respons e or t e on ne system ca rat on, agnos s,an t e commun cat on w t t ese su -mo u es an

ot er contro systems o t e ve c e.

In t e para e y r ve c e, t e motor can raw e ectr c

energy rom t e attery to app y pos t ve torque t at

accelerates the vehicle, and it can supply electric energy

to the battery by applying negative torque that decelerates

he vehicle. These two functions represent torque assist

and regeneration, respectively. The parallel hybrid

e c e contro er must eter m ne ow to str ute

t e r ver s s ng e torque request nto separate torque

requests or t e eng ne, motor, an ra es. For negat vetorque requests, the sum of the motor and brake torques

must equal the drivers request. For positive torque

g. Diagram of the hybrid dynamic system

E-Pedal

Sensors

HCU Display

ICE

STARTDrivers

24 V

Battery

Braking

system

ICEDiagnostic

system

BCM

ADM 312 V, dc NIMH

CAN BUS

Switchs

Calibration

system

ISG

MT

DMCM


3/9

utomotive a ety an nergy , o . o.

requests, the sum of the engine and motor torques must

equal the drivers request. During the braking process,

ISG un erta es a tota or a part o t e ra ng torque an

s respons e or c arg ng t e attery pac , meanw e,

t e mec an ca e nergy can e c ange nto c em ca

energy and therefore, the regenerative braking can be

mplemented. The main task of the NFC is to address the

braking torque distribution between ISG and disc brake

for realizing the ABS functionality. In addition, the

torque o ta ne rom t e ICE s mec an ca y coup e

to t e torque pro uce y ISG. T ere ore t e overa

ra ng torque o t e y r e ectr c us s cons ste o

three parts: ISG torque, friction torque of disc brake and

friction torque from engine.

1.2 Structure of braking system

he regulating valve is responsible for the distributionbetween the regenerative brake and mechanical brake

or ABS contro . W e t e propor t ona so eno s

use or t e ra ng orce contro n t e ra ng system

to ma e t e actua r at o o ront ra ng orce an t e

real braking force a fixed value which is called the

distribution coef cient of braking force. The pneumatic

device is adopted in the braking system for city bus, as

s own n F g. 2.

For the convenience of controlling, the ABS function is

ntegrated into the HCU in this paper. The HCU judges

w et er or not t e w ee s ave t e ten ency o oc ngt roug t e r ece erat on rates an s p rat os w en

ra ng. T en t e ISG torque or sc ra e torque s

corrected by the values of ABS factor. The ABS factor is

determined by two parameters: deceleration rate and slip

ratio of wheel. In the braking operation, there usually

ex sts some erence etween us ve oc ty an w ee

spee , an t s erence s e y to cause t e s p o t e

heel.

Normally, the whole cycle of torque increasing, decreasing

and keeping realizes the control of conventional ABS

effectively. However, for the hybrid bus, when bus is at

high or middle velocity, total or most of the braking torque

s o ere y ISG an t e ra ng a r pressure s qu teow at t at t me. So t e HCU must rect y t e torque o

ISG ot er t an t e sc ra e to rea ze t e ABS c rc e

aforementioned. When bus is at low velocity, the anti-lock

control could be achieved by rectifying the torque of disc

brake by changing the air pressure of the pneumatic braking

system.

2 Modeling and Control Strategies

T e ree o y agram o y r e ectr c c ty us can

be viewed in Fig. 3. The numerical data are provided

n Table 1. The variables for the torque analysis and the

braking model are listed in Table 2.

In the braking system, the proportional solenoid valve for

the braking force control is equipped to make the actual

ratio of F to F into a fixed value of . Owing to the

n uence o ABS, t e tota ra ng torque w reac to

t e max ma va ue e ore t e ront w ee s are a out to e

oc e . T e tota ra ng torque can e expresse as:

T T1 +

bmaR. (1)

Regulatingvalve

Driving line

Proportional valve

Wheel speed sensor Brake cylinder

g. Diagram of braking system

G

hg

a b

L

FZ1

F1

FZ2

F2

ig. 3 ree body diagram of hybrid electric city bus


4/9


5/9


through neural networks. Conventional fuzzy controller

set up rules by using fuzzy logic to imitate human

t oug ts. T e uzzy r u es are a arge gat er o I -t en sentences w c occup es arge amount o memory

capac ty an ncrease s t e computat on u r en. T e

neuro-fuzzy controller combining fuzzy algorithm and

neural networks set up rules by using networks abilityof self-study and intersperses the rules in network and

produces results by high-speed concurrent calculat ion

nstea o comp cate searc an reason ng o ru es

w en runn ng.

F g. 5 s ows a s mp e structure o t e neuro- uzzy

contro er. Data o us ve oc t es, trave o ra e pe a

an s p rat o o w ee are co ecte or comput ng t e

outputs of the neuro-fuzzy controller.

In t e contro strateg es, t e us ve oc ty, t e trave o

ra e pe a an ts var ance rat o are synt es ze as

factors influencing the torque distribution. The ABS

factor determined by the deceleration rate of wheel and

ts slip ratio is used to adjust the output values of torque

st r ut on. T e ma n concept ons o t e st rateg es are

presente as o ows:

1) If SOC (State of charge) of battery pack were too high

SOC>0.9 , t e regenerat ve ra ng s ou e stoppe to

protect t e attery rom overc arg ng.

2) If the bus velocity were too low, regenerative braking

s ou e stoppe to ensure t e ra ng secur ty.

3) If the bus velocity were very high, the braking torque

o ISG s ou e ooste to ts g e c ency zones an

t e at tery pac s c arge y ISG.

4) If the brake pedal varied very fast, the conditions

can e u ge as urgent ra e or qu c re ease o ra e

pedal. In the first condit ion, dr iving secur ity is prior

to regenerat ve ra ng an t e regenerat on s ou

e temporar y cease . In ot con t ons, ISG torque

should be reduced to zero.

5) If the travel of brake pedal normally increased or

decreased, ISG torque should be gradually boosted orre uce accor ng to t e ra e pe a an ept n ts g

ef ciency zone, and the battery pack is cha ged by ISG.

6 T e rotary spee o transm ss on s a t s ca cu ate

upon the bus velocity and the current gear. If the rotary

speed were lower than 600r/min, ISG torque should be

re uce to zero.

7 T e ABS actor var es rom 2 to 2. Its pos t ve va ues

mean increasing braking torque and the negative values

mean decreasing braking toque. The value of zero means

eep ng ra ng torque nvar a e.

8 I us ve oc ty were g er t an 40 m , t e ABS

actor s ou e g ven to rect y t e negat ve torque o

ISG. Otherwise, it should be given to rectify the friction

orque of disc brake by changing oil pressure.

hese main concepts are written asIf-thensentencesan ta en as t e uzzy ru es or computat on. It nee s

no ess t an 300 uzzy ru es to rea ze t e regenerat ve

braking.

The torque range of ISG is divided into five zones

as the first fuzzy output. Zones of 14 are used fore regenerat ve ra ng. Zone 5 represents t e zero

output and the regenerative braking is stopped. The

ABS factor is the second fuzzy output which transfers

comman s o ncreas ng, eep ng or ecreas ng ra ng

torque o us rom HCU to ICM w en us s at g or

iddle speed) or to pneumatic device (when bus is at

ow spee . T ere s on y one except on: w en urgent

or a rupt ra e occurs, ABS actor s t e on y uzzy

output given to the air pressure valve regardless the

ore or less of the speed level of bus.

he NFC has four fuzzy inputs: bus velocity (u), slip

rat o o w ee , trave o ra e pe a S an ts

ariance ratio (d ). All the fuzzy membership functions

are represented in Fig. 6.

The feed-forward back-propagation network is used

n the paper. The input layer has 19 neurons (Xn

n 1~19 , c or r es po n n g t o t e 19 u zz y n pu t

subsets, and its output layer has 10 neurons (T

1~10), corresponding to the 10 fuzzy output subsets.

Determ nat on o t e num er o en un ts an

learning rate is important for the BP neural network.Too few hidden units and too high a learning rate willFig.5 Structure of neuro-fuzzy controller

Fuzzy rules

Neural network

Input layer

Output layerHidden layer

Input

fuzzy

subsets

Output

fuzzy

subsets

FLC


6/9


result in poor learning performance. Too many hidden

units and too low a learning rate will take unacceptable

training time and the many derived weights cannot be

reliably estimated from the available training data. As a

tra e-o a out t e earn ng per ormance an t e tra n ng

t me, t e num er o en un ts s set as 39 T l

1~39 . T e earn ng rate can e set to 0.2.

e FLC use n t s paper cons sts o t e ru e ase,

u zz cat on, net wor tra n ng, u zzy n erence, an

e uzz cat on. M n mum ru es an com nat on

methods, and the center of gravity (COG) methods

are used in the fuzzy inference and defuzzification,

respectively.

e tra n ng ata were generate y s mu at ng t e uzzy

controller alone with the corresponding inputoutput

signals. Matlab/simulink based on back propagation

training algorithm was used to train the network. After

extensive training, the fuzzy controller was replaced

by the neu ral network controller in the dr ive system

simulation.

3 Simulation

T e y r e ectr c c ty us s es gne or c ty pu c

tra c an ts max ma spee s 80 m . T e spee

o 60 m s se ecte or t e s mu at on an t ree

representative braking situations are chosen from the

driving cycle. Table 3 shows the related parameters of

the driving cycle, where the peak deceleration rates

selected are relatively small for simulating the some little

congeste tra c. A 45 A n c e meta y r e NIMH

attery pac s se ecte as t e energy storage system.

The simulating results are shown as Fig. 7. Where, Fig. 7 a

s ows t e genera torque st r ut on resu ts etween

ISG an sc ra e o ront w ee . T e compar sons o

attery SOC see F g. 7 are resu t ng rom two erent

controllers: the neuron-fuzzy controller and the fuzzyogic controller. In the situations of 1 and 3, the intentions

Fig. 6 uzzy membership functions

very-low low middle

Input variable Bus-velocity

Range Range

Range Range

Range Range

Factor

Factor

Factor

high very-high1

0.5

0

0 50 100

Output variable ISG-Trq

1

0.5

00 2 3 4 5

Output variable ABS-factor

1

0.5

0-2 -1 0 1 2

Input variable Variance-ratio-of-Brake-pedal

veryslow

slow fastmiddle very-fast1

0.5

00 50 100

Input variable Slip-ratio

zero small middle laroe1

0.5

00 0.1 0.2 0.3 0.4

Input variable Bus-velocity

very-low low middle high very-high1

0.5

0

0 50 100

Fuzzy inputs

Fuzzy outputs

FLC(Mamdani)

Defuzzification

Fuzzyrules and

BP networks


7/9


of driver are to apply the brake gradually but not abruptly

to stop the bus from some velocity. It can be seen from

these results that each ISG torque is reduced to zero after

o er ng negat ve torque or ra ng an su sequent y t e

ra ng torque s o ere y t e r ct on torque o sc

ra e so as to ensure t e ra ng sa ety, an meanw e,

the rotary speed of ISG is too low to charge the battery.

Oppositely, the intention of driver of situation 2 is just to

decelerate other than to stop the bus from some values

o ve oc ty. T ere ore t e resu ts are erent rom t e

s tuat ons o 1 an 3.

All the braking situations of china city cycle have

re at ve y ow n t a ve oc ty, so t e ntens ty o ra ng

represente y t e ece erat on rate s a so ow a t oug

t e ece erat on rate o 0.158 n t e s tuat on o 3 s

the most one in the five situations. In the situation of

3, the torque of disc brake reaches to over 10 kNm and

therefore the wheels lockup would be likely to occur if

the bus is being driven in the low attachment coef cient

roa s suc as snow roa j 0.15 .

Besides the purpose of delivering the functionality

of ABS, the improvement of the performance of

egenerative braking should st i l l be worthy of

cons erat on or y r us. T s mprovement s c e y

represente y SOC o at te ry pac . In t e prem se

o ra ng sa ety an goo ra ng per ormance, t e

regenerative power recuperation is maximized by the

proposed algorithm for improving fuel economy. The

some few rule application errors (if have) for the braking

o e r ng on y s g t n uence on t e per ormance

o t e ra ng regenerat on. It can e seen rom F g. 7

t at t e c anges o SOC approac to t e va ues resu t ng

from fuzzy logic algorithm.

4 Experiments and Discussions

4.1 Simulating experiments of anti-lock braking

For t e eva uat on o t e genera ty an e ect veness

of the proposed BP neural network based NFC model,

a validation procedure about the NFC based ABS of

able 3 Braking situations of china city cycle

ituationVelocity / (km h

-1

/ s Peak deceleration rate / (m s-2

Max torque / (MNm)rom o

. . .

2 6.6 20 15 .23 1.623

. . .

0.52

0.508

0.510

0.506

0.504

0.502

0.500

0.51

0.50

SOC

SOC

SOC

50 510 1015 1520 25t/ s t/ s

Fuzzy logicNeuro-fuzzy

0.52

0.51

0.505 10 15 20

t/ s



situation 1 situation 2 situation 3

(b) Comparisons of computation error nd fficiency between neuro-fuzzy controller and fuzzy logic ontroller

Fig. 7 Simulating experimental results

0

50 10 15 20 25 0 05 10 15 5 10 15 20

00

0.5

-500

1.0

1.5

-20

-40

-60

-80

0

1.0

2.0

-400

-100

-100

0

-5

-10

-200

-200

-300

-300

ISG

torque/(Nm)

ISG

torque/(Nm)

ISG

torque/(Nm)

Discbraketorque/(kNm)



t/ s t/ s t/ s

situation 1 situation 2 situation 3

(a) Braking torque distribution between ISG nd isc brake f front wheel


8/9


the hybrid electric city bus was formed to carry out

a simulating test about the ABS of the hybrid bus

on a sur ace t rans era e roa w t t wo at tac ment

coe c ents o 0.8 an 0.1 see F g. 8 . Procee ng w t

s tuat on 3 o ra ng o C na c ty cyc e, t e us was

driving in high attachment coef cient of 0.8 at first in the

period of 0 to 12 s after braking, then the road surface

was switching to the low attachment coefficient of 0.1

and lasted for 7 s, and final the bus was stopped at the 19

s. T e torque o sc ra e ecrease to a out 6.5 Nm

rom 11.0 Nm or ant - oc ra ng n t e per o o 12 s

per ormance an t e ra ng regenerat on can ot e

ensured to get to be optimized through the NFC for

vehicle safety and fuel economy.

References

[1] Georg F M. A fuzzy logic controller for ABS braking

system . rans uzzy yst, , ): - .

e son , a oo , c auc an , et a . mp ementat on

o uzzy og c or an ant - oc ra e system rocee ngs

of the 1997 IEEE In Conf on Computational Cybernetics and

Simulations, 1997, : - .

[3] Nakamura E, Soga M, Sakai A. Development of electronicallycontro e ra e system or y r ve c e . aper,

- .

at asiri W, Wickramarachchi N, Halgamuge S K. An

optimized anti-lock braking system in the presence of multiple

road surface types [J]. nt J Adaptive Control and Signal

rocessing, , ): - .

sa , ugeno , erano . pp e uzzy ystems .

ew or : ca em c ress .

[6] Kamiya M, Ikeda H, Shinohara S, Yoshida H. Torque control

strategy for a parallel-hybrid vehicle using fuzzy logic [J].

IEEE Indu Appl Maga, , 6: - .

c oute , a man , e r . uzzy og c controor para e y r ve c es . rans on ontro yst

Fig.8 Simulating experiments of anti-lock braking of

ront wheel of the hybrid bus on the road surfaces

ith different attachment coefficients

0

-100

-2

0

-4

-6

-8

-10

-12

-200

-300

-4000 5 10 15 20

Torque(ISC)

/(Nm)

Torque(brake)/(kNm)

t/ s

to 19 s.

.2 Experiments of city driving cycle

For the evaluation of the efficiency of the regenerative

braking of the hybrid buses, a great lot of experiments for

the regenerative braking have been done in a city driving

cyc e. It can e seen rom F g.9 t at t e requency o

t e start an stop o t e y r us s very g an t e

requent regenerat ve ra ng s ou e one so as to

achieve a better fuel economy. The experimental results

show that the performance of regenerative braking can be

mproved through the proposed NFC model in the paper.

Conclusions

A mo e o an n te gent ant - oc ra ng contro

o y r uses t roug a neuro- uzzy contro er s

presented in the paper. By extensive train ing of the

BP neural network to memorize the fuzzy rules and

combining the fuzzy logic algorithm, the NFC is

mp emente or a ress ng t e torque str ut on

etween ISG an sc ra e. T e exper menta resu ts

s ow t at t e str ut on o ra ng torque or ABS y

NFC is reasonable and effective. Therefore, the braking

0.500

20

40

60

80

1.5 2.5

1

0

-1

key onspeedelevation

Ele

vation/m

Spee

d/(kmh-1)

t/ (103 s)

(a) Operating duty f the hybrid bus in a ity driving cycle

I

/A

0

200

100

0

-100

-2001 2 3

t/ (103 s)

(c) Battery current in city rive cycle

ig. 9 Experiments for regenerative braking

SOC

t/ (103 s)

0.75

0.70

0.65

0.60

0.550 1 2 3000

(b) SOC ustainable ontrol in a ity drive cycle


9/9


ec , 2002, 460-68.

[8] Bathaee S M T, Gastaj A H, Emami S R, et al. A fuzzy-

based supervisory robust control for parallel hybrid electric

ve c es roc o t e con erence on ve c e

power an propu s on, : - .

c outen , a man , e r . uzzy og c contro or

parallel hybrid vehicles [J]. rans on ontro ystec , , (3): 460-68.

[10] Kumar K S, Verghese L, Mahapatra K K. Fuzzy logic ase

ntegrate contro o ant - oc ra e system an co s on

avo ance system us ng or e ectr c ve c es roc

of the 2009 IEEE international conference on industrial

technology, 2009: 1-5.

[11] Jeen-Shing Wang, Lee C S G. Self-adaptive recur rent

neuro- uzzy contro o an autonomous un erwater ve c e

. rans on o otics an uto, , - .

mara , r sostomo . res . e copter

mo ion control using adaptive neuro-fuzzy inference

controller [C]// IEEE Conf of Indu Electronics Soc, 2002, 3

- .

u ota , o ma , o ma , u u a . u t -o ect ve

e av or coor nate or a mo e ro ot w t uzzy neura

networks [C]// Proc of the IEEE-INNS-ENNS Int JointConf on Neural Networks, 2000, : - .

[14] Pepijn V, Tor J, Asgeir S, et al. Neural network augmented

ent cat on o un erwater ve c e mo e s . ontro

ng ractice, , ): - .

[15] Mohebbi M, Charkhgard M, Farrokhi M. Optimal neuro-

fuzzy control of parallel hybrid electric vehicles [C]// Proc

of the 2005 IEEE Conf on Vehicle Power and Propulsion,

, - .

Intelligent+Anti Lock+Braking+Control+of+Hybrid+Buses

Documents

Transcript of Intelligent+Anti Lock+Braking+Control+of+Hybrid+Buses