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World Electric Vehicle Journal is published by MDPI from Volume 9 issue 1 (2018). Articles in this Issue were published by The World Electric Vehicle Association (WEVA) and its member the European Association for e-Mobility (AVERE), the Electric Drive Transportation Association (EDTA), and the Electric Vehicle Association of Asia Pacific (EVAAP). They are hosted by MDPI on mdpi.com as a courtesy and upon agreement with AVERE.
Open AccessArticle

Research on Genetic-fuzzy Control Strategy for Parallel Hybrid Electric Vehicle

1
School of Transportation Science & Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China
2
Great Wall Automobile Co.,Ltd. New Energy Laboratory, 2266 South Street, Chaoyang District, Baoding, Hebei Province 071000, China
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2010, 4(1), 224-231; https://doi.org/10.3390/wevj4010224
Published: 26 March 2010
PDF [687 KB, uploaded 18 May 2018]

Abstract

Fuzzy control strategy is developed for the dual-clutch single-axis torque coupling parallel hybrid electric vehicle. In this paper the torque distribution fuzzy controller which has been designed for the hybrid vehicle which is optimized by genetic algorithms. The simulation model of the hybrid vehicle was built upon matlab / simulink and ADVISOR software. Then a fuzzy rules and correspondent membership functions had been established and the input language variable and output language variable use trapeziform and deltoid membership functions. After design of fuzzy logic torque controller, the genetic algorithm was introduced and used it to optimize the fuzzy logic torque controller. Under typical condition NEDC, the fuzzy control strategy is optimized both by genetic algorithms with the constraint condition of economy performance and by integrated constraint conditions of economy performance and emission performance. Optimization results show that when the controller is only optimize fuzzy control strategy for economy performance the fuel consumption decreased by 5.3% but the emission of CO and NOx both increased, but when the controller both optimize fuzzy control strategy for economy performance and emission performance the fuel consumption decreased by 4.3% with emission quality improved. So the fuzzy control strategy optimize by the genetic algorithm can improve the fuel consumption obvious.
Keywords: Parallel hybrid electric vehicle; genetic algorithm (GA); fuzzy control; optimization Parallel hybrid electric vehicle; genetic algorithm (GA); fuzzy control; optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, S.; Li, M.; Weng, H.; Liu, B.; Li, Q.; Zhu, Y.; Liu, X. Research on Genetic-fuzzy Control Strategy for Parallel Hybrid Electric Vehicle. World Electr. Veh. J. 2010, 4, 224-231.

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