An Adaptive Spatio-Temporal Graph Structure Learning Model for Lithium-Ion Battery Pack State of Health Estimation

Current research on lithium-ion battery state of health (SOH) estimation predominantly focuses on a single battery, not on an entire battery pack, which makes these methods inadequate for describing the SOH of energy systems that work in real-life situations. Furthermore, the current SOH estimation methods using graph neural networks (GNNs) inherently adopt suboptimal graph construction approaches, which makes them fail to accurately extract the most pertinent spatial dependencies, resulting in a reduction in prediction accuracy. In this work, we propose a pioneering GNN-based model to predict battery pack SOH. Specifically, an optimal graph structure, learned by the introduced optimal graph extractor, is used to capture the feature dependencies that are most appropriate for the downstream SOH prediction task. Additionally, a Graph Attention network (GAT) and a Gated Recurrent Unit (GRU) are employed to learn the spatial and temporal features, respectively. Lastly, we introduce an effective and simple spatio-temporal feature fusion module to ensure that the extracted features are fully utilized, and the fused features are then used for prediction. Experimental results on the NASA and CALCE datasets validate the superiority of the proposed approach over existing state-of-the-art methods for battery SOH estimation.