Multi-objective optimization of hybrid electric vehicle control strategy with genetic algorithm

被引：12

作者：

Zhang, Xin ^{[1
]}

Song, Jianfeng ^{[1
]}

Tian, Yi ^{[1
]}

Zhang, Xin ^{[1
]}

机构：

[1] School of Mechanical Electric and Control Engineering, Beijing Jiaotong University

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2009年 / 45卷 / 02期

关键词：

Hybrid electric vehicle; Multi-objective genetic algorithm; Optimization of control strategy;

D O I：

10.3901/JME.2009.02.036

中图分类号：

学科分类号：

摘要：

Hybrid electric vehicle(HEV) is a very complicated non-linear system, whose performance is affected by lots of control parameters. To optimize this system, the routine optimization approach is inefficient, and the reliability of optima depends on the precision of model. An HEV bus dynamic simulation model is built for performance analysis by the interlinking of advanced software AVL CRUISE and MATLAB/Simulink. For achieving minimum fuel consumption and emissions, a multi-objective genetic algorithm(GA) optimization method is applied to getting the optima of work modes and energy distribution in different city bus cycles, which finds the compatible control logic parameters and saves much time. The feasible solution can improve the fuel economy and emissions simultaneously and provide wider choices for different requirements in HEV design.

引用

页码：36 / 40

页数：4

共 9 条

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