Optimization of HEV system parameters based on hybrid adaptive genetic algorithm - 文章详情页

作者：

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

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摘要：

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.

关键词：

Parameter estimation Optimization Genetic algorithms Hybrid vehicles Electric vehicles

作者机构：

[ 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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