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汽车安全与节能学报

汽车安全与节能学报 ›› 2014, Vol. 5 ›› Issue (04): 324-330.DOI: 10.3969/j.issn.1674-8484.2014.04.002

• 汽车安全 • 上一篇    下一篇

基于正交试验设计和多目标遗传算法的HEV 参数优化

周云山，贾杰锋   

  1. 湖南大学 汽车电子与控制技术教育部工程研究中心，长沙 410082，中国
  • 收稿日期:2014-09-18 出版日期:2014-12-25 发布日期:2014-12-29
  • 作者简介:周云山（1957 －），男（汉），湖南，教授。E-mail: zys_8888@sina.com
  • 基金资助:

    国家“八六三”高技术研究发展计划（2012AA111710）

Parameters optimization of hybrid electric vehicle based on orthogonal experimental design and multi-objective genetic algorithm

ZHOU Yunshan, JIA Jiefeng   

  1. Engineering Research Center of Automotive Electronics and Control Technology of Ministry of Education, Hunan University, Changsha 410082, China
  • Received:2014-09-18 Online:2014-12-25 Published:2014-12-29

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摘要：

为在满足动力性前提下，降低混合动力汽车（HEV）的油耗和排放，提出了一种新的参数优化
方法。以ADVISOR 为仿真平台，应用正交试验设计，找出了对油耗和排放性能影响最显著的5 个动
力系统部件及控制策略的系统参数。建立了HEV 多目标优化模型。用多目标遗传算法和最小二乘意义
下的主客观组合赋权法，得到该模型的Pareto 最优解集合，并从中选出了最优参数组合。结果表明：
与优化前相比较，优化后的参数下，每100 km 的油耗降低25.3%，每1 km 的CO 的排放质量降低
35.5%，每1 km 的HC+NOx 的排放质量降低13.7%。因而，验证了该方法的有效性。

关键词: 混合动力汽车\正交试验设计\多目标遗传算法, Pareto 最优, 参数优化

Abstract:

A parameter optimization method for hybrid electric vehicle (HEV) was proposed to improve fuel
economy and reduce emission within requisite power performances. An orthogonal experimental design was
used with ADVISOR platform to find out the first fifth notable system parameters, which severely influence
the fuel economy and emission of HEV, among power components and control strategies. An optimization
model was built using a multi-objective genetic algorithm to obtain a set of Pareto-optimal solution. An optimal
parameter combination from the solution set was selected using a combination weighting method between
subjective and objective evaluation in a least squares sense. The results show that with the optimized
parameters, the fuel consumption per 100km is reduced by 25.3%, the CO emissions per kilometer is reduced
by 35.5%, and the total HC and NOx emission is reduced by 13.7%. These facts verify the effectiveness of the
method.

Key words: hybrid electric vehicle (HEV), orthogonal experimental design, multi-objective genetic algorithm, Pareto optimality, parameters optimization