Optimization of Electric Vehicle Battery Charging Strategy Based on Reinforcement Learning

The battery capacity of electric vehicles (EVs) is a critical factor influencing their overall performance. Selecting an appropriate charging strategy based on the EV driver’s travel behaviors can significantly mitigate battery capacity degradation and prolong its lifespan. This study integrated a physics-based battery degradation model with an EV powertrain system energy consumption model to enhance the prediction accuracy for the battery status under real-world driving conditions. In addition, it employed the Q-learning algorithm to provide suggestions on the driver’s charging behaviors, including charging start time, charging duration, and charger types. To create a reasonable reward function in the Q-learning algorithm, this study addressed the issue of car rental costs when the battery capacity is insufficient to support daily mileage. With the algorithm, this study modeled the 3-year use of an EV by an average driver sampled from the New England area in the U.S. The simulation results show that the charging strategy using reinforcement learning can mitigate the battery degradation rates by 1.53‰, 3.57‰, and 7.68‰ than those charging behaviors with Level 2 only, direct-current Fast Charge [DCFC] only, and extreme Fast Charging [xDC] only, respectively. The algorithm created in this study shows that an AI-integrated charging strategy can help improve the battery health status while lowering the operational costs of EVs and satisfying the driver's travel demands, thereby enhancing the overall economic feasibility of EV usage in its usage lifecycle.