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Author:

Liu, Xu-Dong (Liu, Xu-Dong.) | Fan, Qing-Wu (Fan, Qing-Wu.) | Duan, Jian-Min (Duan, Jian-Min.) (Scholars:段建民) | Zhou, Da-Sen (Zhou, Da-Sen.)

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Abstract:

Selecting optimization algorithm is an important part in the optimization of HEV system parameters. To overcome the shortcomings of prematurity and slow convergence speed in simple genetic algorithm, a hybrid adaptive genetic algorithm is proposed. Testing results show that the proposed algorithm has both good whole astringency and fast convergence speed. The new hybrid adaptive genetic algorithm is applied to the optimization of HEV system parameters and satisfactory optimization results and convergence effects are obtained. Based on the optimization results, an engine/generator set of a series hybrid electric mini-bus is designed optimally.

Keyword:

Parameter estimation Optimization Genetic algorithms Hybrid vehicles Electric vehicles

Author Community:

  • [ 1 ] [Liu, Xu-Dong]Pilot College of Beijing University of Technology, Beijing 101101, China
  • [ 2 ] [Fan, Qing-Wu]Pilot College of Beijing University of Technology, Beijing 101101, China
  • [ 3 ] [Duan, Jian-Min]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Zhou, Da-Sen]Pilot College of Beijing University of Technology, Beijing 101101, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2009

Issue: 7

Volume: 35

Page: 904-909

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 11

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