DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention

Abstract

Accurately predicting the State of Health (SOH) of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems, such as electric vehicles and renewable energy grids. The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators. Existing methods often fail to capture the dynamic interactions between health indicators over time, resulting in limited predictive accuracy. To address these challenges, we propose a novel framework, Dynamic Graph Learning with Spatial–Temporal Fusion Attention (DGL-STFA), which transforms health indicator time-series data into time-evolving graph representations. The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns, a self-attention mechanism to construct dynamic adjacency matrices that adapt over time, and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation. This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies, enhancing SOH prediction accuracy. Extensive experiments were conducted on the NASA and CALCE battery datasets, comparing this framework with traditional time-series prediction methods and other graph-based prediction methods. The results demonstrate that our framework significantly improves prediction accuracy, with a mean absolute error more than 30% lower than other methods. Further analysis demonstrated the robustness of DGL-STFA across various battery life stages, including early, mid, and end-of-life phases. These results highlight the capability of DGL-STFA to accurately predict SOH, addressing critical challenges in advancing battery health monitoring for energy storage applications.