Optimization of Fuzzy Controller Based on Genetic Algorithm

The power required to drive the Hybrid electric generated by combination of internal combustion engine and electric motor. To make the power train of the hybrid electric vehicle as efficient as possible, proper management of the different energy elements is essential. This task is completed by the hybrid electric vehicle control strategy. A genetic-fuzzy control strategy is proposed for Hybrid electric vehicle in this paper. The genetic-fuzzy controller is a fuzzy logic controller that is tuned by a genetic algorithm. The objective of optimization is to decrease fuel consumption and emissions in two different test cycles NEDC and UDDS, the results demonstrate that compared with fuzzy logic control strategy, genetic-fuzzy control strategy can get better control effects. The effectiveness of this approach can reduce fuel consumption and emissions without sacrificing vehicle performance.

[1]  John Daniel. Bagley,et al.  The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[2]  A. Piccolo,et al.  Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms , 2001, 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Proceedings (Cat. No.01TH8556).

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

[4]  Mehmet Kaya,et al.  Determination of fuzzy logic membership functions using genetic algorithms , 2001, Fuzzy Sets Syst..

[5]  K. T. Chau,et al.  Modern Electric Vehicle Technology , 2001 .

[6]  S Z Qin,et al.  Comparison of four neural net learning methods for dynamic system identification , 1992, IEEE Trans. Neural Networks.

[7]  Jing Zhu,et al.  Neural network based fuzzy identification and its application to modeling and control of complex systems , 1995, IEEE Trans. Syst. Man Cybern..

[8]  Yi-Sheng Zhou,et al.  Optimal design for fuzzy controllers by genetic algorithms , 2000 .

[9]  K. De Jong Learning with Genetic Algorithms: An Overview , 1988 .

[10]  Fei Shi,et al.  Optimization of membership function for fuzzy control based on genetic algorithm and its applications , 1998 .

[11]  Morteza Montazeri,et al.  Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles , 2008 .

[12]  John H. Holland,et al.  Outline for a Logical Theory of Adaptive Systems , 1962, JACM.