A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learning

Abstract

This paper presents an innovative strategy that utilizes reinforcement learning to enhance the fast balance charging of lithium-ion battery packs. We develop an interactive framework for lithium-ion batteries by utilizing an electro-thermal coupled model that incorporates hysteresis and temperature impacts. This framework provides precise simulation within a user-friendly gym environment. We propose a novel priority-objective reward function to address the joint challenge of battery pack balancing and fast charging. This reward function is then integrated into the Soft Actor–Critic (SAC) algorithm, delivering an optimal solution within a unified problem framework for the first time. Through a series of comprehensive simulations, we validate the effectiveness of our approach in terms of both performance and computational costs. Our method consistently achieves accelerated charging times, maintains a uniform cell state of charge distribution, and minimizes safety hazards.