摘要
以混合电动车 ( HEV)的性能仿真软件 ADVISOR为平台 ,应用一种高效的多目标演化算法——非占优排序遗传算法 ( NSGA-II) ,将一辆并联 HEV的百公里油耗和 HC,CO,NOx的排放等4个目标同时进行了优化 ,优化变量同时包含了部件尺寸参数和能源管理策略参数 ,得到了一组Pareto解 .针对该 Pareto解集的分析表明 ,在不牺牲动力性的前提下 ,NSGA- 大大提高了原车的经济性能和排放性能 ,并且为
Based on the hybrid electric vehicle (HEV) simulator, ADVISOR, the paper treats the component sizes of a parallel HEV together with the energy strategy parameters as variables, and optimizes its fuel consumption and HC, CO, NOx emissions simultaneously, using one of the efficient multi-objective evolutionary algorithms, non-dominated sorting genetic algorithm (NSGA-Ⅱ). The obtained Pareto-optimal set shows that NSGA-Ⅱ improves the fuel economy and reduces the emissions of the original HEV without sacrifice of vehicle performances. It is more important that the Pareto-optimal set provides a wide range of choices for the HEV design as well as control.
出处
《武汉理工大学学报（交通科学与工程版）》
北大核心
2004年第3期384-387,共4页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
教育部重点项目资助 (批准号 :0 2 175 )
