| 基于MOEA的PHEV控制策略与参数优化 |
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| 引用本文： | 杨观赐,李少波,璩晶磊,潘伟杰.基于MOEA的PHEV控制策略与参数优化[J].华中科技大学学报(自然科学版),2012,40(5):114-119,128. |
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| 作者姓名： | 杨观赐 李少波 璩晶磊 潘伟杰 |
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| 作者单位： | 1. 贵州大学现代制造技术教育部重点实验室,贵州贵阳550003／中国科学院成都计算机应用研究所,四川成都610041
2. 贵州大学现代制造技术教育部重点实验室,贵州贵阳,550003 |
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| 基金项目： | 教育部新世纪优秀人才支持计划资助项目，国家高技术研究发展计划资助项目，贵州省科学技术基金资助项目 |
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| 摘 要： | 针对并联混合动力汽车（PHEV）控制策略与传动系统参数优化问题,建立了以最小化燃油消耗与污染物排放量的多目标优化模型,研究基于多目标进化算法的PHEV控制策略与传动系统参数优化方法.采用ADVISOR仿真并将仿真所得的燃油消耗与污染物排放量作为候选方案的目标值,基于帕累托支配性原理判定候选方案的优劣,限定待优化变量的取值在PHEV生产精度要求范围内.实验结果显示：优化后的系统1×105m燃油消耗平均降低了22.32%,污染物CO,HC与NOx排放量平均降低了22.06%,7.98%和7%;电动机工作效率由0.18提高到0.52以上;系统总效率平均提升了22.99%.
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| 关 键 词： | 多目标优化 进化算法 混合动力汽车 控制策略 传动系统 |
Control strategy and parameters optimization of PHEV using MOEA |
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| Yang Guanci,Li Shaobo,Qu Jinglei,Pan Weijie.Control strategy and parameters optimization of PHEV using MOEA[J].JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE,2012,40(5):114-119,128. |
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| Authors: | Yang Guanci Li Shaobo Qu Jinglei Pan Weijie |
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| Institution: | 1(1 Key Laboratory of Advanced Manufacturing Technology,Ministry of Education, Guizhou University,Guiyang 550003,China;2 Chengdu Institute of Computer Applications,Chinese Academy of Sciences,Chengdu 610041,China) |
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| Abstract: | Aimed at parameter optimization of parallel hybrid electrical vehicles(PHEV)and control strategy of drivetrain,the mathematical model of multi-objective optimization to minimize the fuel consumption and emissions was presented,and a methodological approach to solve the problem was proposed based on multi-objective evolutionary algorithm(MOEA).In this method,the Pareto dominance principle was employed to judge candidate solutions,and the objectives were minimum fuel consumption and exhaust emissions achieved by ADVISOR simulation,and the value of optimization variables was limited to meet the accuracy of PHEV production requirements.The simulation optimization results show that compared with the original system the fuel consumption 1×105 m fall on average by 22.32%,and the CO,HC and NOx emission reduces by 22.06%,7.98%,7% on average;at worst the motor work efficiency increases from 0.18 to above 0.52;the overall efficiency of PHEV is improved on average by about 22.99%. |
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| Keywords: | multi-objective optimization evolutionary algorithm hybrid electrical vehicles control strategy drivetrain |
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