Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) generate the power required to drive the vehicle via a combination of internal combustion engines and electric generators. To make HEVs as efficient as possible, proper management of the different energy elements is essential. This task is performed using the HEV control strategy. The HEV control strategy is the algorithm according to which energy is produced, used and saved. This paper describes a genetic-fuzzy control strategy for parallel HEVs. The genetic-fuzzy control strategy is a fuzzy logic controller that is tuned by a genetic algorithm. The objective is to minimize fuel consumption and emissions, while enhancing or maintaining the driving performance characteristics of the vehicle. The tuning process is performed over three different driving cycles including NEDC, FTP and TEH-CAR. Results from the computer simulation demonstrate the effectiveness of this approach in reducing fuel consumption and emissions without sacrificing vehicle performance.

[1]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[2]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[3]  Seung-Ki Sul,et al.  Torque control strategy for a parallel hybrid vehicle using fuzzy logic , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

[4]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[5]  Gregory N. Washington,et al.  Mechatronic design and control of hybrid electric vehicles , 2000 .

[6]  Amory B. Lovins,et al.  Vehicle Design Strategies to Meet and Exceed PNGV Goals , 1995 .

[7]  Reza Langari,et al.  Fuzzy torque distribution control for a parallel hybrid vehicle , 2002, Expert Syst. J. Knowl. Eng..

[8]  Angelo Raciti,et al.  Energy flows management in hybrid vehicles by fuzzy logic controller , 1994, Proceedings of MELECON '94. Mediterranean Electrotechnical Conference.

[9]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[10]  Mutasim A. Salman,et al.  Energy management strategies for parallel hybrid vehicles using fuzzy logic , 2000 .

[11]  R. P. Jones,et al.  Energy management in an automotive electric/heat engine hybrid powertrain using fuzzy decision making , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[12]  Tony Markel,et al.  ADVISOR: A SYSTEMS ANALYSIS TOOL FOR ADVANCED VEHICLE MODELING , 2002 .

[13]  Charles L. Karr,et al.  Genetic algorithms for fuzzy controllers , 1991 .

[14]  K. T. Chau,et al.  Overview of power management in hybrid electric vehicles , 2002 .

[15]  Mutasim A. Salman,et al.  Emissions and fuel economy trade-off for hybrid vehicles using fuzzy logic , 2004, Math. Comput. Simul..

[16]  Keith Wipke,et al.  HEV Control Strategy for Real-Time Optimization of Fuel Economy and Emissions , 2000 .

[17]  Mutasim A. Salman,et al.  Fuzzy logic control for parallel hybrid vehicles , 2002, IEEE Trans. Control. Syst. Technol..

[18]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Amir Poursamad,et al.  Optimization of Component Sizes in Parallel Hybrid Electric Vehicles via Genetic Algorithms , 2005 .