基于遗传算法的HEV控制策略优化

李军; 徐杨蛟; 朱亚洲; 纪雷

doi:10.13433/j.cnki.1003-8728.2015.1121

基于遗传算法的HEV控制策略优化

doi: 10.13433/j.cnki.1003-8728.2015.1121

1.
重庆交通大学机电与汽车工程学院, 重庆 400074

基金项目:

重庆市科委自然基金项目(7994-2)、重庆市教委自然科学项目(KJ090408)及重庆交通大学研究生教育创新基金项目(20120108)资助

详细信息

作者简介:
李军(1964-),教授,博士,研究方向为汽车发动机排放和控制,cqleejun@163.com

计量
- 文章访问数: 175
- HTML全文浏览量: 25
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2013-01-07
- 刊出日期: 2015-11-05

Optimizing HEV Control Strategy Based on Genetic Algorithm

1.
School of Mechatronics & Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China

摘要

摘要: 混合动力汽车控制策略参数的选取对系统性能有重大的影响。为此,建立了以动力性能为约束指标、以燃油消耗率和排放为评价指标的混合动力汽车控制策略参数优化模型,并对控制策略参数进行优化。利用加权系数法整合评价指标,结合遗传算法工具箱GATBX对控制策略参数进行多目标优化,并利用ADVISOR对优化前后的性能进行仿真。仿真结果表明:在保证动力性的前提下有效降低了混合动力汽车燃料消耗量和排放,其中油耗和NOx降低了17%,HC和CO降低了大约7%。
- 混合动力汽车(HEV) /
- 控制策略 /
- 遗传算法 /
- 多目标优化
Abstract: The selection of parameters for a hybrid electric vehicle (HEV) control strategy has a significant influence on its performance. An optimization model for the HEV control strategy is set up to optimize the parameters of the HEV control strategy, including a constraint indicator of power performance and an evaluation indicator of fuel consumption and emissions. Since the parameter optimization for the HEV control strategy is a multi-objective optimization problem, evaluation indicators are integrated into one objective function by weighting their coefficients. GATBX (Genetic Algorithms Tool Box) was used to optimize the parameters for the HEV control strategy, and the performance of the optimized HEV was simulated by ADVISOR. The simulation results and their analysis show that the optimized control strategy can effectively reduce fuel consumption and emissions without sacrificing power performance and fuel consumption and that NOx emission is reduced by 17% and that HC and CO emissions are reduced by about 7%.
- computer simulation /
- computer software /
- controllers /
- efficiency

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参考文献(10)

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