Classification of Lithium-Ion Batteries Based on Impedance Spectrum Features and an Improved K-Means Algorithm

This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, parameter identification the fractional order model is proposed, which based on flow direction (FDA). In to reduce dimensionality of battery features, Pearson correlation coefficient employed analyze between features. The carried out using improved algorithm, incorporates optimization initial clustering center grey wolf (GWO) algorithm. experimental results demonstrate effectiveness this in accurately classifying batteries its high level accuracy robustness. Consequently, can be relied upon provide robust support performance evaluation fault diagnosis.