Fusion of Local and Global Feature-Based Explainable Diagnostic Method for Lithium-Ion Battery Degradation Modes

A fusion of local and global feature-based explainable diagnosis methods is proposed for lithium-ion battery (LIB) degradation modes in this article. The method captures fault features from the discharge curves of a semi-battery model. Voltage curves are transformed into incremental capacity (IC) curves, converted into 2-D images and subjected to feature extraction to obtain high-dimensional features. A battery fault feature-extraction module based on a local-global feature fusion module is designed to enhance feature saliency by extracting long-term dependency information. To explain the learning process, a battery fault explainability determination module is designed to analyze the attention points of the proposed method on the overall IC curve image changes by highlighting the focus areas. The explainability analysis of the decision-making process was accomplished by integrating the IC curve analysis, including variations in the IC peaks, to establish a physical correlation. Finally, the effectiveness and superiority of the proposed method were verified through experiments.