An Adaptive Control Framework for Dynamically Reconfigurable Battery Systems Based on Deep Reinforcement Learning.

This article presents an adaptive control framework for dynamically reconfigurable battery (DRB) systems based on the deep reinforcement learning method. The proposed adaptive control framework relies on deep Q-network to learn the DRB system operations. By utilizing its model-free nature, the proposed framework can significantly reduce the complexity of building experiences or expert models for DRB systems as well as improve battery operating time by ensuring cell balancing. Extensive simulation and experimental study has been carried out with data gathered from a real-world DRB testbed, and the results show the effectiveness and efficiency of the proposed control framework. [ABSTRACT FROM AUTHOR]