Cooperative Cell Balancing For Supercapacitors With Reinforcement Learning

With the rapid advancement of technologies such as electric vehicles, the demand for energy storage devices has surged, leading to the widespread adoption of supercapacitors due to their numerous advantages. In practical applications, supercapacitors are often arranged in series or parallel configurations to form capacitor banks, which cater to higher voltage or capacity requirements. However, inconsistencies in the manufacturing processes and materials can lead to variations in the electrical performance of individual supercapacitors, necessitating effective balance management. Existing balancing methods are generally categorized into passive and active approaches. While passive balancing circuits are simple and cost-effective, they tend to be inefficient. On the other hand, active balancing methods, although offering high control precision, are typically more complex and expensive. This paper introduces a collaborative balancing strategy based on Deep Deterministic Policy Gradient (DDPG) using a switch resistor circuit, which serves as an intermediate approach between passive and active methods by combining their respective advantages. By integrating deep reinforcement learning with the switch resistor circuit for supercapacitor balancing, the proposed method addresses the slow balancing speed of traditional circuits under significant voltage disparities, enhancing the robustness of the balancing process and achieving superior performance. A simulation environment is established in Simulink to evaluate the effectiveness of the proposed method under various initial voltage conditions. The results demonstrate that the supercapacitor bank achieves balance within a short time frame. Moreover, comparative experiments indicate that the collaborative strategy significantly reduces overshoot and improves the robustness of supercapacitor balancing compared to noncollaborative approaches.