基于工况与粒子群优化的增程汽车能量管理策略开发  

Development of Energy Management Strategy for Extended Range Vehicle Based on Driving Condition and Particle Swarm Optimization

作　　者：闫德超 马超[1] 杨坤[1] 谭迪[1] YAN De-chao;MA Chao;YANG Kun;TAN Di(School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

机构地区：[1]山东理工大学交通与车辆工程学院,淄博255000

出　　处：《科学技术与工程》2021年第26期11396-11404,共9页Science Technology and Engineering

基　　金：国家自然科学基金(51605265);山东省重点研发计划(2019GGX104069)。

摘　　要：为了进一步提高增程式电动汽车(extended range electric vehicle,EREV)的燃油经济性,在满足驾驶性能和车辆动力要求的前提下,根据低速、中速、高速典型工况下发动机功率分布分析,提出一种基于自适应权重粒子群算法(adaptive weighted particle swarm optimization,AW-PSO)优化的三点式最优功率控制策略。为验证其经济性能,基于MATLAB/Simulink开发动力系统模型以及整车能量管理策略。基于驱动成本理论,在多种国际标准工况下进行仿真对比,结果表明:相比功率跟随策略而言,基于工况的三点式功率控制策略实现平均12.95%的成本节省,而AW-PSO优化策略下平均节约成本提升到21.44%。In order to further improve the fuel economy of the extended range electric vehicle(EREV),a three-point optimal power control strategy based on adaptive weighted particle swarm optimization(AW-PSO)was proposed according to the analysis of engine power distribution under typical working conditions of low,medium and high speeds under the premise of satisfying the driving performance and vehicle power requirements.In order to verify its economic performance,power system model and vehicle energy management strategy were developed based on MATLAB/Simulink.Based on the driving cost theory,simulation comparison was carried out under various international standard operating conditions.The results show that compare with the power following strategy,the three-point power control strategy based on operating conditions achieve an average cost savings of 12.95%,while the average cost savings under the AW-PSO optimization strategy increase to 21.44%.

关 键 词：工况分析 增程式电动汽车 能量管理策略 粒子群算法 驱动成本理论 仿真对比 

分 类 号：U469.72[机械工程—车辆工程]