基于多目标优化的增程式电动汽车自适应制动回馈控制策略  

Adaptive Regenerative Braking Control Strategy of Range-Extended Electric Vehicle Based on Multi-Objective Optimization

作　　者：刘汉武[1] 雷雨龙[1] 付尧[1] 李兴忠[1] LIU Hanwu;LEI Yulong;FU Yao;LI Xingzhong(State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, Jilin, China)

机构地区：[1]吉林大学汽车仿真与控制国家重点实验室,吉林长春130022

出　　处：《华南理工大学学报（自然科学版）》2021年第7期42-50,65,共10页Journal of South China University of Technology(Natural Science Edition)

基　　金：吉林省科技发展计划项目(20170204073GX,20180520071JH);青岛市科技计划项目(18-1-2-17-zhc);吉林省教育厅“十三五”科学技术研究规划项目(JJKH20200957KJ)。

摘　　要：针对增程式电动汽车制动回馈控制策略多目标优化问题,基于多目标优化模型和最优优化理论,提出了一种基于多目标参数优化结果的自适应制动回馈控制策略。首先,基于AVL/Cruise和Matlab/Simulnk软件搭建整车系统仿真控制模型,并基于NSGA-Ⅱ算法,以系统制动效能、制动回馈能量和电池容量衰减率为目标函数构建多目标优化模型;然后仿真离线优化得到综合再生制动性能指标下的制动工作点切换门限值Parato最优解,并结合仿真优化结果设计了自适应模糊控制器,控制器考虑了路面附着系数和动力电池的荷电状态,可在线实时调整制动工作点的分配。WLTP循环工况下的仿真结果表明,该自适应制动回馈控制策略可以有效平衡制动效能、制动回馈能量和电池容量衰减率之间的关系,在有效提高制动效能和制动回馈能量的同时维持较小的电池容量衰减率。Aiming at the multi-objective optimization(MOO)problem of the range-extended electric vehicle rege-nerative braking control strategy,a real-time adaptive regenerative braking control strategy was proposed based on the MOO model and optimal optimization theory.Firstly,the vehicle simulation model was established on AVL/Cruise and Matlab/Simulnk software,and a MOO model was built with the system braking performance(BP),regenerative braking loss efficiency(RBLE)and battery capacity loss rate(BCLR)as the objective functions based on NSGA-Ⅱalgorithm.Then Parato optimal solution was obtained through off-line optimization under the comprehensive regenerative braking performance.Combined with the optimization results,a real-time adaptive fuzzy controller was designed.The controller considers the road adhesion and the state of battery,and can adjust the distribution of the regenerative braking work-point online.Simulation results on WLTP driving cyclic conditions show that the adaptive regenerative braking control strategy can effectively balance the relationship among BP,RBLE and BCLR,and it can effectively reduce BP and RBLE while maintaining a small BCLR.

关 键 词：增程式电动汽车 制动回馈 NSGA-Ⅱ算法 多目标优化 自适应模糊控制 

分 类 号：U270[机械工程—车辆工程]