GNN-Based Surrogate Model for Reconfigurable Battery Packs

This paper presents a novel Graph Neural Network (GNN)-based surrogate model 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, our GNN model predicts 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 demonstrate that our GNN-based approach significantly outperforms baseline Feedforward Neural Network (FNN) and FNN-attention models, showing substantial improvements in prediction accuracy for both temperature and SOC while maintaining robust performance even with limited training data.