Optimizing Discharge Efficiency of Reconfigurable Battery With Deep Reinforcement Learning

Cell imbalance in a multicell battery occurs over time due to varying operating environments. This imbalance leads to overall inefficiency in battery discharging due to the relatively weak cells in the battery. Reconfiguring the cells in the battery is one option for addressing the problem, but relevant circuits may lead to severe safety issues. In this article, we aim to optimize the discharge efficiency of a multicell battery using safety-supplemented hardware. To this end, we first design a cell string-level reconfiguration scheme that is safe in hardware operations and also provides scalability due to the low switching complexity. Second, we propose a machine learning-based run-time switch control that considers various battery-related factors, such as the state of charge, state of health, temperature, and current distributions. Specifically, by exploiting the deep reinforcement learning (DRL) technique, we train the complex relationship among the battery factors and derive the best switch configuration in run-time. We implemented a hardware prototype, validated its functionalities, and evaluated the efficacy of the DRL-based control policy. The experimental results showed that the proposed scheme, along with the optimization method, improves the discharge efficiency of multicell batteries. In particular, the discharge efficiency gain is maximized when the cells constituting the battery are unevenly distributed in terms of cell health and exposed temperature.