Surrogate Model for Reconfigurable Battery Packs Using Graph Neural Networks

This article presents a novel graph neural network (GNN)-based surrogate model (GNN-SM) for predicting state evolution in reconfigurable battery packs. By leveraging graph-based representations of battery cell interconnections, the proposed approach addresses the unique challenge of estimating the imbalance in state-of-charge (SOC) and temperature of cells of a battery pack in dynamic battery configurations. Unlike conventional methods that focus on instantaneous state estimation, ourGNN-SMpredicts future SOC and temperature distributions by considering both current system state and switch configuration. The model architecture combines graph attention networks with pooling operations to effectively capture cell-to-cell interactions and battery pack-level dynamics. Numerical results under constant current and constant power discharge conditions demonstrate thatGNN-SMsignificantly outperforms baseline feedforward neural network (FNN) and FNN-attention models, achieving up to 73.7% reduction in root-mean-square error for temperature imbalance prediction and 46% reduction for SOC imbalance prediction. Furthermore, the model provides a 1629-fold speedup over high fidelity physics-based simulator while maintaining mean absolute percentage errors below 2% for temperature and 8% for SOC predictions. The scalability ofGNN-SMis further validated on a 100-cell reconfigurable battery pack, where the proposed approach achieves high accuracy despite being trained on an extremely small fraction of all possible configurations. Finally, robustness analysis under sensor noise conditions demonstrates thatGNN-SMmaintains reliable predictions even under high noise levels.