Soft clustering of retired lithium-ion batteries for the secondary utilization using Gaussian mixture model based on electrochemical impedance spectroscopy

Abstract

Rapid sorting and reasonable regrouping of retired lithium-ion batteries (LIBs) are directly related to the economy and safety of the second-life utilization. However, the efficiency and accuracy of sorting for the retired LIBs needs to be further improved, and the regrouping method is still in the exploratory stage. In this study, a soft clustering method based on the Gaussian mixture model (GMM) using electrochemical impedance spectroscopy (EIS) is proposed to address these issues. In this method, the multi-dimensional clustering criteria are extracted from EIS, and the capacity is quickly estimated based on the EIS using a neural network. Furthermore, the ageing factors of six criteria are constructed to realize the soft clustering of retired cells corresponding to three ageing modes. The simulation results show that it only takes about 10 min to obtain the capacity of each cell, and the error is within 4%. Moreover, the clustering probability of each cell under different ageing modes is obtained using GMM, which is useful for flexible grouping of cells. Finally, the proposed methods are evaluated by experiments, and results show that the consistency of the regrouped cells using the proposed soft-clustering method is nearly doubled than that of the random regrouped cells.