Fast-Charging Optimization Method for Lithium-Ion Battery Packs Based on Deep Deterministic Policy Gradient Algorithm

Fast-charging technology for lithium-ion batteries is of great significance in reducing charging time and enhancing user experience. However, during fast charging, the imbalance among battery cells can affect the overall performance and available capacity of the battery pack. Moreover, the charging efficiency not only is limited by the battery technology itself but is also closely related to the optimization of the charging strategy. To address the optimization of fast charging for lithium-ion batteries, this paper proposes a method based on deep reinforcement learning. First, a deep reinforcement learning charging optimization model is constructed, aiming to minimize charging time and SOC balancing cost, with constraints on battery voltage, temperature, SOC, and SOH. The model employs the deep deterministic policy gradient (DDPG) algorithm integrated with reward centralization and entropy regularization mechanisms, aiming to dynamically adjust the charging current to achieve an optimal balance between fast charging and battery health. Experimental results indicate that the proposed method enhances charging efficiency, contributes to extending battery life, and supports the safety of the charging process. Compared to the traditional constant-current constant-voltage (CCCV) strategy, the improved DDPG strategy reduces the total charging time by 60 s and the balancing time from 540 s to 470 s. Furthermore, compared to the basic DDPG method, the proposed algorithm shows a clear advantage in charging efficiency.