ANN-Enhanced Modulated Model Predictive Control for AC-DC Converters in Grid-Connected Battery Systems

Andrea Volpini, Samuela Rokocakau, Giulia Tresca, Filippo Gemma and Pericle Zanchetta ()
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Andrea Volpini: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
Samuela Rokocakau: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
Giulia Tresca: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
Filippo Gemma: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
Pericle Zanchetta: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy

Energies, 2025, vol. 18, issue 15, 1-17

Abstract: With the increasing integration of renewable energy sources (RESs) into power systems, batteries are playing a critical role in ensuring grid reliability and flexibility. Among them, vanadium redox flow batteries (VRFBs) have emerged as a promising solution for large-scale storage due to their long cycle life, scalability, and deep discharge capability. However, achieving optimal control and system-level integration of VRFBs requires accurate, real-time modeling and parameter estimation, challenging tasks given the multi-physics nature and time-varying dynamics of such systems. This paper presents a lightweight physics-informed neural network (PINN) framework tailored for VRFBs, which directly embeds the discrete-time state-space dynamics into the network architecture. The model simultaneously predicts terminal voltage and estimates five discrete-time physical parameters associated with RC dynamics and internal resistance, while avoiding hidden layers to enhance interpretability and computational efficiency. The resulting PINN model is integrated into a modulated model predictive control (MMPC) scheme for a dual-stage DC-AC converter interfacing the VRFB with low-voltage AC grids. Simulation and hardware-in-the-loop results demonstrate that adaptive tuning of the PINN-estimated parameters enables precise tracking of battery parameter variations, thereby improving the robustness and performance of the MMPC controller under varying operating conditions.

Keywords: physics-informed neural networks (PINNs); vanadium redox flow battery (VRFB); model predictive control (MPC); state-space modeling; parameter estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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