Research on External Short Circuit Fault Evaluation Method for Li-Ion Batteries Based on Impedance Spectrum Feature Extraction

Accurate evaluation of the severity of external short-circuit (ESC) faults in li-ion batteries is critical to ensuring the safety and reliability of battery systems. This study proposes a novel ESC fault assessment method based on electrochemical impedance spectroscopy (EIS) and differential feature extraction from relaxation time distributions. By comparing EIS responses before and after the short circuit, differential curves are constructed, and relevant peak descriptors are extracted to form physically interpretable feature vectors without requiring equivalent circuit modeling. Standardized feature data are further analyzed using principal component analysis (PCA) and K-Means clustering to perform unsupervised classification of fault severity. In addition, a differential evolution algorithm is employed to adaptively optimize the feature weights, enhancing the monotonic correlation between the weighted scores and actual short-circuit durations. The resulting SeverityScore provides an interpretable, mechanism-driven indicator of ESC fault severity. Experimental results demonstrate that the proposed method effectively distinguishes between mild and moderate short-circuit conditions and generalizes well across four independent battery groups. The model, trained on a single group, demonstrates strong robustness by accurately classifying the fault severity for three unseen validation groups. This data-driven framework offers a robust and model-free approach for fault evaluation, providing a promising tool for health monitoring and risk assessment in li-ion batteries.