增程式电动汽车参数匹配及控制策略多目标协同优化  

Multi-objective Collaborative Optimization of Parameter Matching and Control Strategy for Extended-Range Electric Vehicles

作　　者：肖仁鑫[1] 黄彪 贾现广[1] 吕英英[2] XIAO Renxin;HUANG Biao;JIA Xianguang;LV Yingying(Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,China;Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学交通工程学院,云南昆明650500 [2]昆明理工大学信息工程与自动化学院,云南昆明650500

出　　处：《昆明理工大学学报（自然科学版）》2022年第5期103-112,共10页Journal of Kunming University of Science and Technology(Natural Science)

基　　金：国家自然科学基金项目(51567012);云南省万人计划青年拔尖人才培养项目(KKRD201902062).

摘　　要：为提高增程式电动汽车综合性能和市场竞争力,提出了增程器三工作点改进控制策略,采用非支配排序遗传算法,以动力性能、综合能耗及装配成本为目标函数,对动力系统部件及控制策略进行协同优化.从Pareto最优解集中选择3个典型的方案,对3个方案的电机及发动机工作点、加速时间、SOC及电量消耗进行对比分析.研究结果表明:在满足约束条件时,NEDC工况下动力性能最大提高1.37%时,其能耗增加了3.43%;装配成本最大减少6.84%时,动力性能最大降低1.38%;当综合能耗最大降低2.96%时,其动力性能与装配成本指标介于其他2个方案之间.CLTC工况下,动力性能最大提高1.29%时,其能耗增加了3.10%;装配成本最大减少7.07%时,动力性能最大降低1.80%;当综合能耗最大降低3.01%时,其动力性能与装配成本指标同样介于其他2个方案之间.增程式电动汽车在CLTC工况下具有更高效的能耗经济性,更适合中国城市道路工况行驶.对EREV部件参数及控制策略参数同时优化,在提高车辆动力性能和经济性能的同时,能对装配成本合理控制,提高EREV的综合性能与市场接受度.In order to realize the collaborative optimization of the power system component parameters and control strategy parameters of the extended-range electric vehicle(EREV), and the three-working-point improvement control strategy of the range extender is proposed. Based on NSGA-II multi-objective optimization algorithm, the power system component parameters and control strategy parameters are optimized collaboratively with power performance, comprehensive energy consumption and assembly cost as the objective functions, and three typical solutions are selected from the Pareto optimal solution set, and the motor and engine operating points, acceleration time, SOC and power consumption of the three solutions are compared and analyzed. The results show that, under the constraints, the power performance increases by 3.43% for a maximum improvement of 1.37% under NEDC condition, and the power performance decreases by 1.38% for a maximum reduction of 6.84% in assembly cost, and the power performance and assembly cost indexes are between the other two schemes when the overall energy consumption is reduced by 2.96%. The power performance and assembly cost indexes are also between the other two options when the maximum reduction in energy consumption is 3.10% for a 1.29% increase in power performance and the power performance is reduced by 1.80% when the assembly cost is reduced by 7.07%, and the power performance and assembly cost are reduced by 3.01% when the power performance and assembly cost indexes are between the other two scenarios. The EREV has a more efficient energy economy under CLTC condition, which is more suitable for Chinese urban road conditions. Simultaneous optimization of the EREV component parameters and control strategy parameters can improve the vehicle power performance and economic performance and reasonably control the assembly cost and improve the overall performance and market acceptance of the EREV.

关 键 词：增程式电动汽车 控制策略 参数匹配 多目标优化 

分 类 号：U469.72[机械工程—车辆工程]