Fast selection method for retired power batteries based on three-decision clustering
The inconsistency issue of retired batteries during cascaded utilization significantly affects battery performance and safety.Efficient selection methods can effectively mitigate this inconsis-tency.Firstly,addressing the inefficiency in obtaining clustering features for retired batteries,this pa-per proposes a method to extract new clustering features from IC curves at 4 C current rate.Sec-ondly,due to the fact that clustering results from efficient binary decision clustering algorithms often do not favor battery selection consistency,this paper adopts an improved K-means clustering algo-rithm based on three-decision clustering.Additionally,a grid partition correction strategy combining a local gravity model is proposed to accurately identify core objects in the three-decision process.Re-sults demonstrate that the proposed method exhibits good performance in screening efficiency and battery selection consistency.
retired batteriesfast sortingincremental capacity analysisthree-decision clusteringgrid divisionlocal gravity model
万方数据
维普
