Optimization of Electric Vehicle Charging and Discharging Strategies Considering Battery Health State: A Safe Reinforcement Learning Approach - Shuifu Gu; Kejun Qian; Yongbiao Yang (World Electric Vehicle Journal)
With the widespread adoption of electric vehicles (EVs), optimizing their charging and discharging strategies to improve energy efficiency and extend battery life has become a focal point of current research. Traditional charging and discharging strategies often fail to adequately consider the battery’s state of health (SOH), resulting in accelerated battery aging and decreased efficiency. In response, this paper proposes a safe reinforcement learning–based optimization method for EV charging and discharging strategies, aimed at minimizing charging and discharging costs while accounting for battery SOH. First, a novel battery health status prediction model based on physics-informed hybrid neural networks (PHNN) is designed. Then, the EV charging and discharging decision-making problem, considering battery health status, is formulated as a constrained Markov decision process, and an interior-point policy optimization (IPO) algorithm based on long short-term memory (LSTM) neural networks is proposed to solve it. The algorithm filters out strategies that violate constraints by introducing a logarithmic barrier function. Finally, the experimental results demonstrate that the proposed method significantly enhances battery life while maintaining maximum economic benefits during the EV charging and discharging process. This research provides a novel solution for intelligent and personalized charging strategies for EVs, which is of great significance for promoting the sustainable development of new energy vehicles.