Multi-stage deep sorting strategy for retired batteries based on the clustering of static and dynamic features

Abstract

Echelon utilization is the optimal solution for handling retired lithium-ion batteries. How to ensure the economy and safety of retired batteries for echelon utilization, consistency sorting technology is crucial and valuable. However, batteries are influenced by multiple factors and the mapping of internal and external parameters is complex. Finding external features with discrimination and interpretability, and using effective sorting methods for consistency sorting is still challenged. To address the above problem, a multistage deep sorting strategy for retired batteries based on the clustering of static and dynamic features is proposed in this paper. Firstly, the density-based spatial clustering of applications with noise is used to remove abnormal batteries in the initial sorting stage, which ensures the safety of battery echelon utilization and the efficiency of energy utilization. Secondly, low-correlation static features are selected by Pearson correlation coefficient analysis, and the WK-means clustering algorithm was employed for the secondary sorting of retired batteries. Finally, the sliding window method is used to extract the time-square dynamic features from the dynamic stress test data, and the clustering algorithm is employed to subdivide the retired batteries again with precision. The experimental results indicate that the proposed method in this paper can effectively achieve consistency assessment and sorting of retired batteries. After sorting, the capacity, resistance, and open-circuit voltage of the battery are compactly distributed, resulting in a reduction of their corresponding standard deviations by 36.76 %, 62.97 %, and 71.62 %, respectively. Furthermore, the cut-off time and voltage drop of dynamic profile are reduced by 8.87 % and 65.36 %, respectively.