Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning

Applied Sciences

Renxin Xiao¹  Zheng Chen¹  Yitao Wu¹  Jiangwei Shen¹  Hengjie Hu¹  Yonggang Liu² 

[1] Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China;State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China;

关键词: energy management strategy;    Markov decision process (MDP);    plug-in hybrid electric vehicles (PHEVs);    Q-learning (QL);    reinforcement learning (RL);   

DOI  :  10.3390/app8122494

来源: DOAJ

【 摘 要 】

This paper proposes an energy management strategy for a power-split plug-in hybrid electric vehicle (PHEV) based on reinforcement learning (RL). Firstly, a control-oriented power-split PHEV model is built, and then the RL method is employed based on the Markov Decision Process (MDP) to find the optimal solution according to the built model. During the strategy search, several different standard driving schedules are chosen, and the transfer probability of the power demand is derived based on the Markov chain. Accordingly, the optimal control strategy is found by the Q-learning (QL) algorithm, which can decide suitable energy allocation between the gasoline engine and the battery pack. Simulation results indicate that the RL-based control strategy could not only lessen fuel consumption under different driving cycles, but also limit the maximum discharge power of battery, compared with the charging depletion/charging sustaining (CD/CS) method and the equivalent consumption minimization strategy (ECMS).

【 授权许可】

Unknown