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

作　　者：黄欣[1] 陈凌珊[1] 程伟[2] 孙逸神[2] 张晓杰[2] 

机构地区：[1]上海工程技术大学汽车工程学院,上海201620 [2]上海汽车集团股份有限公司前瞻技术研究部,上海201804

出　　处：《计算机测量与控制》2015年第10期3539-3542,共4页Computer Measurement &Control

基　　金：上海市科委"面向商业化的强混动力系统开发"课题

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。Based on the completion of powertrain parameter matching and performance simulation of extended--range electric vehicle （E --REV）, a linear weighted multi--objective genetic algorithm was desinged in view of the optimization of E REV＇ s powertrain parameter matching, in which parameters of drive motor＇ s peak power, engine＇ s rated power and battery＇ s energy were optimized with the targets of manufacturing costs, equivalent fuel consumption and acceleration time per 100 km. Results showed that the vehicle＇ s manufacturing costs and fuel consumption can be reduced by 5. 19% and 9.61% mostly at the expense of appropriate sacrifices of dynamic. Therefore the vehicle＇ s dynamic and economic performance can be further improved and manufacturing costs be reduced by suitable matching scheme, which played a great significance on E--REV＇ s production in the market.

关 键 词：增程式电动汽车 参数匹配 多目标优化 

分 类 号：U469.7[机械工程—车辆工程]