基于混合自适应遗传算法HEV系统参数的优化

刘旭东; 范青武; 段建民; 周大森

基于混合自适应遗传算法HEV系统参数的优化

1. 北京工业大学实验学院, 北京 101101;
2. 北京工业大学电子信息与控制工程学院, 北京 100124

基金项目:

北京工业大学青科基金资助项目(X1024000200801).

详细信息

作者简介:
刘旭东(1975一),男,河北石家庄人,讲师.

中图分类号: U469.72
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- 文章访问数: 18
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出版历程
- 收稿日期: 2008-02-20
- 网络出版日期: 2022-12-09

Optimization of HEV System Parameters Based on Hybrid Adaptive Genetic Algorithm

1. The Pilot College of Beijing University of Technology, Beijing 101101, China;
2. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

摘要

摘要: 选择优化算法是混合动力电动汽车系统参数优化的一个重要内容.针对基本遗传算法存在着易早熟、收敛速度慢的缺陷,提出了一种混合自适应遗传算法.测试结果表明,该算法既具有良好的全局收敛性,又具有较快的收敛速度.将该算法应用到混合动力电动汽车系统参数优化问题中,取得了较为满意的优化结果和收敛效果.根据优化结果,对一辆串联式混合动力中巴的发动机/发电机组进行了优化设计.
- 电动汽车 /
- 混合动力 /
- 遗传算法 /
- 系统参数 /
- 优化
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.
- electric vehicle /
- hybrid power /
- genetic algorithm /
- system parameters /
- optimization

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参考文献(10)

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基于混合自适应遗传算法HEV系统参数的优化

作者简介: 刘旭东(1975一),男,河北石家庄人,讲师.

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出版历程

Optimization of HEV System Parameters Based on Hybrid Adaptive Genetic Algorithm

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出版历程

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作者简介:
刘旭东(1975一),男,河北石家庄人,讲师.