摘要
为提高增程式电动车的自适应能量管理策略中工况辨识的准确性,通过分析样本特征参数与庞特里亚金极小值原理最优协态变量的相关性,选择有效特征参数,经过主成分分析后,建立样本数据库。对车辆进行在线工况辨识,将辨识的最优协态变量经过电池SOC修正后作为车辆当前协态变量,实现对车辆能量管理策略的自适应优化。结果表明,相比当前工况辨识策略,所设计的自适应PMP能量管理策略可使油耗降低、SOC变化更加稳定、动力电池寿命延长。
In order to improve the accuracy of driving pattern identification in the adaptive energy management strategy of extended-range electric vehicle(EREV),the effective characteristic parameters were selected by analyzing the correlation between the sample characteristic parameters and the optimal co-state variables of Pontryagin's minimum principle(PMP).The sample database was established after the principal component analysis of effective characteristic parameters.In order to realize the adaptive adjustment of PMP energy management strategy,the on-line driving pattern identification was carried out,and the identified optimal co-state variable was taken as the current co-state variable after the correction of battery SOC.The results show that the fuel consumption of the designed adaptive PMP energy management strategy is lower than that of the current driving pattern identification strategy.The SOC change is more stable,which can extend the life of power battery.
作者
顾琰浩
吴晓东
许敏
GU Yanhao;WU Xiaodong;XU Min(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《车用发动机》
北大核心
2021年第1期1-8,共8页
Vehicle Engine
基金
科技部国家重点研发计划资助(2018YFB0106000)。
关键词
增程式电动车
工况辨识
自适应控制
能量管理
extended-range electric vehicle(EREV)
driving pattern identification
adaptive control
energy management
作者简介
顾琰浩(1991-),男,硕士,研究方向为混合动力车辆能量管理,guyanhao@sjtu.edu.cn;通讯作者:吴晓东(1984-),男,副教授,博士生导师,研究方向为混合动力车辆能量管理,xiaodongwu@sjtu.edu.cn。
