基于NSGA-Ⅱ算法的混合动力汽车多目标优化

Multi-objective Optimization of Hybrid Electric Vehicle Based on NSGA-Ⅱ Algorithm

针对当前混合动力系统的研究主要集中在基于燃油经济性的单目标优化或者是加权的多目标优化,未能从本质上体现出整车性能目标之间的耦合关系的情况,以某款并联四驱混合动力汽车的动力性和经济性为优化目标,采用NSGA-II算法对整车传动系统参数进行匹配优化。在保证整车基本性能的前提下,100 km加速时间最大缩短了10.79%,100 km油耗最大下降了14.82%,100 km电耗最大下降了8.39%。得到的Pareto解集为整车设计及优化提供了更合理的选择空间,也为混合动力多目标权衡控制策略提供了理想的控制基础。

The research on current Hybrid Electric Vehicle （HEV） mainly focuses on single-objective optimization based on fuel economy or muhi-objective optimization based on weighting, which fails to essentially reflect the coupling relationship belween vehicle performance targets. Taking the power and economy of a HEV with parallel tour-drive as the optimization objective, the NSGA-Ⅱ algorithm is used to optimize the parameters of whole vehicle transmission system. Under the premise of ensuring the basic performanee of vehicle, the acceleration time of 100 km was shortened by 10.79%, the thel consumption of 100 km decreased by 14.82%, and the power consumption of 100 km decreased by 8.39%. The obtained Pareto solution set provides a more reasonable choice space tbr vehicle design and optimization, and also provides an ideal control basis for hybrid nmlti-objeetive trade-off control strategy.