【2008.08.22】【水】the application of fuzzy-neural network on control strategy of hybrid...

7/27/2019 2008.08.22 The application of fuzzy-neural network on control strategy of hybrid vehicles

1/4

281

Proceedings of the 27th Chinese Control ConferenceJuly 16-18, 2008, Kunming,Yunnan, China

The Application of Fuzzy-Neural Network on Control Strategy of

Hybrid Vehicles

Chen Rongguang 1, Li Chunsheng 2, Meng Xia 3, Yu Yongguang 3,4

1. Department of Electronic Information Engineering, Beihang University, Beijing 100083, P.R.ChinaE-mail: [email protected]

2. Department of Electronic Information Engineering, Beihang University, Beijing 100083, P.R.ChinaE-mail: [email protected]

3. Department of Mathematics, Beijing Jiaotong University, Beijing 100044, P.R.China

E-mail: [email protected]. Department of MEEM, City University of Hong Kong, Kowloon, Hong Kong SAR,P.R. China

E-mail: [email protected]

Abstract: In order to increase the fuel economy and decrease the emissions of hybrid vehicles, firstly a fuzzy logic control

system is presented in this paper. In parallel hybrid vehicles, the whole required torque comes from internal combustion engineand motor engine respectively. Based on the desired torque for driving and state of charge, the fuzzy logic control system de-termines how the power splits between the dual sources, which is the key point for hybrid vehicles. Then, Adaptive Neu-

ral-Fuzzy Inference System (ANFIS) method is applied to optimize fuzzy logic control system based on the data of driving cycle.The main contribution of this paper is well application of fuzzy-neural network to improve original control system, whichminimized the fuel consumption and emissions. The simulation results show very good performance of the proposed method.Key Words: Fuzzy logic controller, Control strategy, Hybrid electric vehicles

1 INTRODUCTION

As one of the most important transportation vehicles,automobiles play an irreplaceable role in peoples daily

life. By the end of 2005, the number of automobilesaround the world had reached 800 million. It is said thisnumber will reach 1000 million in the year 2010

[1]. With

the growing number of cars, the exhaust emissions of

conventional internal combustion engine (ICE) also in-crease insanely, which seriously damage the environmentof human being and consume the limited petroleum.

Then quickly electric vehicles (EVs) attracted the atten-

tion of many vehicle industries because electricity is atotally pollution-free energy. But there are also some

disadvantages for EVs such as short driving distance,restricted peak power and frequent recharging. ThereforeEVs can not replace conventional cars in a short time tosolve the problem.

In 1990s, a large amount of hybrid electric vehicles

(HEVs) was developed to achieve the best compromise

between ICE cars and EVs. Different kinds of HEVswere designed by different companies

[2]. HEVs have two

or more than two power sources: an ICE which convertsfuels to electric or mechanical energy, and an electronicmotor (EM) with a hi-power energy storage system suchas battery pack or a super capacitor

[3]. HEVs consume

less fuel and can reduce emission due to dual sources thanconventional cars. The power sharing between ICE andEM is the key point, which is the most important anddifficult problem to HEVs. Thus lots of control strategiesof hybrid vehicles have been proposed since HEVs ap-

pear[4,5]

.

In the paper, the control strategy is designed based onfuzzy logic control theory and neural network, which arevery suitable to deal with highly nonlinear problem. Withits robustness and adaptability, the control strategy

achieves good fuel economy and low emission per-formance.

The rest of this paper is organized as follows. Section

of the paper describes the model of fuzzy logic controllerand how to apply it to HEV control. ANFIS is explained

to optimize fuzzy controller in section . Followed by

the simulation result and analysis in section . Conclu-

sion and prospect is presented at last in section.

2 FUZZY LOGIC CONTROLLER

Fuzzy logic controller (FLC) does well in non-linearcontrol system because it works as a black box. There isno need to establish an exact model when using FLC. By

identifying the inputs and outputs of FLC, we can make agood control strategy through a series of appropriatefuzzy rules.

2.1 The Structure of Fuzzy Logic Controller

Firstly, we have to identify the inputs and outputs of FLC.

In the control of a HEV, the main goal is to optimize theoperation point of ICE and EM to improve the overallefficiency of the powertrain. Thus The ICE operationmust be set according to the current road load and state of

charge (SOC). Therefore two inputs are imposed in theFLC: the whole desired torque and the battery pack SOC.Based on the above two inputs, FLC gives one output: the

required output torque of ICEICE

T , and the desired

torque out of EM can be given by formula (1).

EM LAOD ICET T T= (1)

WhereLOAD

T is the load required from the driving cycle

due to acceleration, drag, road grade, etc[3]. The wholestructure of designed FLC is depicted in Fig.1.


2/4

282

LOADT

SOC

ICET

EMT+

Fig.1 Structure of fuzzy logic controller

2.2 The Membership Functions and Fuzzy RulesThen, the membership functions and fuzzy rules for FLCshould be set down.

Fig.2 Membership functions of desired torque for driving

Degreeofmembership

Fig.3 Membership functions of SOC

To reflect the states of input and output variable more

precisely according to the experience of expert or theresult of experiment, both inputs are scaled from 1 to

11For SOC, the basic set is [0.55 0.7], where 0.55

would correspond to 1 and 0.7 would correspond to 11,linearly interpolating between them

[6]. For the ICE torque,

1 means very low torque required while 11 refers largetorque required. Also bell-type function that has bettertransitional performance has been chosen instead of tri-angle function. These membership functions have beenmarked as 1 to 11, as shown in Fig.2 and Fig.3.

The FLC described in this paper is of the SugenoTakagitype

[7]. A conceptually different type of FLC is the

Mandami FLC [8], for which the consequent part of therules (the control actions) are also represented by fuzzysets. For Mandamis approach the inference and aggre-

gation act on the fuzzy sets of the consequents instead ofsingle (crisp) values, and the result of both steps is

again a fuzzy set. Defuzzification is needed to replacethis fuzzy set by a single (crisp) value (such as thegravity center of area of the fuzzy set

[9]) that acts as one

of the controller outputs[10]

. So there is no membershipfunction for output variable here. The fuzzy rules are

listed in Tab.1.

Tab.1 The Fuzzy Rules of Controller

(Rows for SOC MFs and columns for desired torque MFs, theoutputs are crisp values for required torque of ICE)

1

2

3

4

5

6

7

8

9

10

11

6

6

6

6

6

7

7

8

9

10

11

6

6

6

6

6

6

7

7

8

9

10

6

6

6

6

6

6

6

7

7

8

9

6

6

6

6

6

6

6

6

7

7

8

5

6

6

6

6

6

6

6

6

6

7

5

5

6

6

6

6

6

6

6

6

6

4

5

5

6

6

6

6

6

6

6

6

3

4

5

5

6

6

6

6

6

6

6

2

3

4

5

5

6

6

6

6

6

6

2

2

3

4

5

5

5

5

6

6

6

1

2

2

3

4

5

5

5

6

6

6

1 2 3 4 5 6 7 8 9 10 11soc

torque

Tab.1actually presents a list of if-then rules that repre-sent the energy management strategy. For example IFSOC=1 and torque=1 THEN torque of ICE=6. Theselinguistic terms reflect human knowledge of the PHVsuch as knowledge of HEV experts or old-time experi-

ence. It means if the SOC is at a low level and the re-quired torque is also at a very low level, then it is the best

for ICE to work at a moderate state that can be in a highefficiency and can charge the battery too.

3 ADAPTIVE FUZZY-NEURAL INFERENCE

SYSTEM

According to the description in section,we know thatFLC is mainly based on peoples experience and intuition.

It can not always achieve satisfied efficiency dependingon different route. ANFIS is a modeling method which

primarily based on data[11]

. The membership functionsand rules of FLC could be optimized after ANFIS is

trained by actual driving cycle data[12,13]

. Therefore weuse ANFIS theory in this section to change the shape ofmembership functions of FLC in order to acquire better

performance[14]

.

The training data is come from the driving cycle model inADVISOR2002 software. In this paper, the purpose is to

minimize the fuel consumption corresponding to thespecial route. The general structure of ANFIS has beenshown in Fig.4.


3/4

283

Fig.4 Structure of ANFIS

4 SIMULATION RESULTS

We have used ADVISOR2002 software that based onSIMULINK in the simulation

[15]. The control block of

vehicle model has been replaced by the FLC designed in

previous section of this paper.

The specific PHV configuration, used throughout thesimulation, consists of the following components given

by Tab.2

Tab.2 Main components of PHV

Internal combustion enginewith spark ignition

41KW

Motor AC 25KW

Lead-Acid battery 25KW

Total test vehicle mass 1334kg

When using FLC, the simulation results are shown in

Fig.5 under UDDS (Urban Dynamometer Driving

Schedule) driving cycle. Through the results we can see

that when SOC is at a high level, EM will provide most of

the torque to meet the required quantity. Otherwise, ICEworks at a very high efficiency region to recharge the

battery if SOC is too low. As a result, we achieve good

fuel economy and maintain SOC within a good area

between 0.55 and 0.7 simultaneously. On one hand, this

leaves enough capacity to handle an extended period of

the battery discharge (such as during a long time accel-

eration) and enough headroom to absorb a long period

of charging (such as during a long downhill). On the other

hand, from the control point of view, the battery SOC is

maintained near a balance point to ensure the system

stability[3]

.

Fig.5 Simulation result of UDDS using FLC

Fig.6 Trained membership functions of torque

Fig.7 Trained membership functions of SOC

Every different driving cycle is characterized by differentroad situation, so we choose a few samples from one

certain driving cycle model like UDDS in this paper totrain the ANFIS in order to achieve optimized FLC cor-responding to UDDS. Points 0-100, 600-700, 900-100 of

UDDS are selected as the samples. Fig.6~Fig.7 show themembership functions after trained.

We can see from the result that the 5th

, 6th

and 7th

mem-


4/4

284

bership functions of torque become wider because op-eration here is high efficient. Membership functions of

SOC change little. The simulation is done again by re-placing the FLC by ANFIS. The simulation result ispresented in Fig.8.

Fig.8 Simulation result of UDDS using ANFIS

It can be found that the new controller utilizes batteryenergy better while keeping SOC in the same right region.

More operation points of ICE work at high efficient arealeading to the result of reducing fuel consumption. Thecomparison of fuel consumption and emission between

FLC and ANFIS is shown in Tab.3. Fuel economy be-comes 49.9 mpg after optimization, increasing 2.3%. Theemission is also reduced comparatively. We can draw theconclusion that the ANFIS is suitable and effective.

Tab. 3 Comparison between FLC and ANFIS

FLCcontroller

ANFIScontroller

Fuel Economy(mpg) 48.8 49.9

HC(grams/mile) 0.45 0.444

CO(grams/mile) 2.089 2.074

NOx(grams/mile) 0.238 0.242

5 CONCLUSION

This paper has discussed a method based on ANFIS todevelop FLC in order to improve the adaptability androbustness of FLC further. Data of driving cycle is usedto train ANFIS to establish the non-linear relationship

between input and output. Simulations show promising

results as compared to conventional FLC. It has the ref-erence value for further investigation of intelligent HEV

controller design.

Future trends and more effective way of control will bethe mixed application of artificial intelligence such as

fuzzy logic, neural network and evolutionary algorithms.

REFERENCES[1] YUAN F G. Information Galaxy. International Market of

Machinery and Electric Equipment, 1995, 12(5): 31.[2] VOELCKER J.Top 10 Tech Cars. IEEE Spectrum,2004,41(3):

28-35.[3] ZENG Q, HUANG J H . The Design and Simulation of

Fuzzy Logic Controller for Parallel Hybrid Electric Vehi-cles[C]. Proceeding of the IEEE International Conference onAutomation and Logistics, Jinan, China, August 18-21, 2007:

908-912.[4] LIN C C, KANG J M, GRIZZLE J W, PENG H. Energy

Management Strategy for a Parallel Hybrid Electric Truck[C].Proceedings of the American Control Conference, Arlington,June 25-27, 2001, 4: 2878-2883.

[5] PAGANELLI G, DELPRAT S, GUERRA T M, RIMAUX J,

SANTIN J J. Equivalent Consumption Minimization Strategyfor Parallel Hybrid Powertrains[C]. IEEE 55th VehicularTechnology Conference VTC Spring 2002, 6-9 May, 2002 ,4:2076-2081.

[6] ADVISOR Help Documentation. America: National Re-

newable Energy Laboratory, April 30, 2002.[7] TAKAGI T, SUGENO M. Fuzzy Identification of Systems

and Its Application to Modeling and Control. IEEE Trans.

System, Man, Cybernetic, 1985, 15: 116132.[8] MANDAMI E H. Applications of Fuzzy Algorithms for

Control of Simple Dynamic Plant. Proc. IEE, 1974:1585-1588.

[9] SHI G, JING Y W, XU A, MA J. Study and Simulation of

Based-fuzzy-logic Parallel Hybrid Electric Vehicles ControlStrategy[C]. Proceedings of the Sixth International Confer-ence on Intelligent Systems Design and Applications(ISDA'06), October, 2006: 280-284.

[10] SCHOUTEN N J, SALMAN M A, KHEIR N A. Fuzzy LogicControl for Parallel Hybrid Vehicles. IEEE Transactions on

Control Systems Technology, 2002, 10(3): 460-468.[11] ROGER JANG J S. ANFIS: Adaptive-Network-Based Fuzzy

Inference System. IEEE Transactions on Systems, Man, Cy-

bernetic, May/June, 1993, 23(3): 665-685.[12] SHAHRI A M, NAGHDY F, NYUYNE P. Neuro-Fuzzy

Compliance Control with the Ability of Skill Acquisitionfrom Human Experts[C]. 1997 First International Conferenceon Knowledge Based Intelligent Electronic Systems, May

21-23, 1997, 2: 442-449.[13] MOHEBBI M, CHARKHGARD M, FARROKHI M. Opti-mal Neuro-Fuzzy Control of Parallel Hybrid Electric Vehi-cles[C].Vehicle Power and Propulsion 2005 IEEE Confer-ence, September 7-9, 2005: 26-30.

[14] BAUMANN B, RIZZONI G, WASHINGTON G. IntelligentControl of Hybrid Vehicles Using Neural Networks andFuzzy Logic. International Congress and Exposition, Detroit,Michigan, February 23-26, 1998: 1-11.

[15] WIPKE K B, CUDDY M R, BURCH S D. ADVISOR 2.1: A

User-Friendly Advanced Powertrain Simulation Using aCombined Backward/Forward Approach. IEEE Transactionson Vehicular Technology, 1999, 48(6):1751-1761.

【2008.08.22】【水】the application of fuzzy-neural network on control strategy of hybrid...

Documents