HEV传动系统多目标优化研究

Research on Multi-objectives Optimization of HEV Transmission System

针对当前混合动力研究主要集中在燃油经济性等单目标上,对多目标研究较少的情况,提出一种基于非支配排序的多目标优化算法(multi-objective evolutionary algorithm,MOEA)。以装备5档手动变速器的并联混合动力汽车为对象,研究传动系速比匹配对燃油经济性与排放性的影响。结果表明:相比优化前,优化后燃油经济性提升了3.09%,排放性综合指标提升17.92%;得到的Pareto解集具有良好的分布性与收敛性,不仅优化了目标,更体现出目标间的冲突情况,说明提出的多目标优化算法能够体现混合动力多目标优化的本质;对解集进一步挖掘,理论上能搜寻到的全局最优解集,为混合动力多目标权衡控制策略提供了理想的控制基础。

At present HEV （Hybrid Electric Vehicle） research mainly focuses on the single object such as fuel economy, but little for multi-objectives. Taking the parallel HEV with 5 gears manual transmission as the research object, a multi-objectives evolutionary algorithm （MOEA） based on non-dominated sorting is proposed, which can analyze the influence of transmission ratio match on the fuel economy and emissions. The simulation results show that fuel economy increases by 3.09% and the emission performance comprehensive index increases by 17.92% compared with the pre-optimization results, which improves the effect of energy conservation. Moreover, the obtained Pareto solution sets have good distribution and convergence, which not only optimizes the objectives, but also reflects the conflict conditions among the objectives. The results show that the proposed MOEA can embody the essence of multi-objectives optimization for HEV. The overall optimal solution set, which can be searched theoretically, provides the ideal control basis for HEV multi-objectives trade-off control strategy.