基于多目标遗传算法的增程式电动汽车动力系统参数匹配优化研究

Optimal Study on Extended-Range Electric Vehicle’s Powertrain Parameter Matching Based on Multi-objective Genetic Algorithm

摘要:

在完成增程式电动汽车（E-REV）动力匹配与性能仿真基础上，针对E-REV动力系统参数匹配优化问题，以整车制造成本、汽车两种运行模式下等效百公里油耗以及百公里加速时间为目标，以驱动电机峰值功率、发动机额定功率以及电池能量为变量，设计了基于线性加权的多目标遗传算法。结果表明，适当牺牲汽车动力性可最大降低制造成本5.19%，并降低等效油耗9.61%以上。可以得出，通过改善匹配方案能进一步提高整车的动力经济性并降低制造成本，研究对E-REV市场推广及量产化具有重要意义。

Abstract:

In view of the optimization of E-REV’s powertrain parameter matching, a linear weighted multi-objective genetic algorithm was proposed based on the completion of powertrain parameter matching and performance simulation, in which parameters of drive motor, battery and engine were optimized with the targets of manufacturing costs, fuel consumption and acceleration time. Results showed that the vehicle’s dynamic and economic performance has been further improved, and manufacturing costs was reduced, which played a guiding significance on E-REV’s production in the market.