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Lookup NU author(s): Professor Volker PickertORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The integration of reconfigurable battery storage(RBS) systems into dc shipboard microgrids (SMG) holds con-siderable potential by improving cell-level flexibility and systemadaptability compared to conventional battery storage systems.This paper proposes an online-trained deep reinforcement learn-ing control (DRL) framework for an RBS system interfacedwith a bidirectional dual-active-bridge (DAB) converter, aimingto manage its dynamic operation while enabling adaptive currentregulation and cell balancing. Considering the nonlinear dynam-ics and real-time uncertainties inherent in practical SMGs, anautomated online training framework is developed to bridge thesim-to-real gap that often hinders the generalization of offline-trained DRL methods. To this end, the proposed online trainingframework is implemented on a dSPACE real-time platform,enabling the controller to continuously interact with the physicalsystem and refine its policy with real-world experiences. Exper-imental results validate that the proposed online-trained DRLapproach achieves convergence 20.1% faster, in terms of trainingepisodes, compared to simulation-based training. Comparativestudies demonstrate that the proposed control strategy achievessuperior 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 enablesfaster charge-discharge transitions, completing mode switcheswithin just 9.1 ms. Furthermore, the approach improves batterybalancing and transient current handling, thereby improving itssuitability for SMG applications
Author(s): Yuan Z, Zeng Y, Ghias A, Pou Josep, Pickert V
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Transportation Electrification
Year: 2026
Pages: epub ahead of print
Online publication date: 12/01/2026
Acceptance date: 03/01/2026
Date deposited: 05/03/2026
ISSN (electronic): 2332-7782
Publisher: IEEE
URL: https://doi.org/10.1109/TTE.2026.3653172
DOI: 10.1109/TTE.2026.3653172
ePrints DOI: 10.57711/dwpa-c028
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