Online-Trained Deep Reinforcement Learning-Based Control for Reconfigurable Battery Storage in DC Shipboard Microgrid Applications

The integration of reconfigurable battery storage (RBS) systems into dc shipboard microgrids (SMG) holds considerable potential by improving cell-level flexibility and system adaptability compared to conventional battery storage systems. This paper proposes an online-trained deep reinforcement learning control (DRL) framework for an RBS system interfaced with a bidirectional dual-active-bridge (DAB) converter, aiming to manage its dynamic operation while enabling adaptive current regulation and cell balancing. Considering the nonlinear dynamics and real-time uncertainties inherent in practical SMGs, an automated online training framework is developed to bridge the sim-to-real gap that often hinders the generalization of offline-trained DRL methods. To this end, the proposed online training framework is implemented on a dSPACE real-time platform, enabling the controller to continuously interact with the physical system and refine its policy with real-world experiences. Experimental results validate that the proposed online-trained DRL approach achieves convergence 20.1% faster, in terms of training episodes, compared to simulation-based training. Comparative studies demonstrate that the proposed control strategy achieves superior dynamic performance, reducing settling time by 43.6% compared to the simulation-trained controller and by 53.7% compared to the benchmark predictive controller. It also enables faster charge-discharge transitions, completing mode switches within just 9.1 ms. Furthermore, the approach improves battery balancing and transient current handling, thereby improving its suitability for SMG applications.