This digest proposes an optimal clustering algorithm considering performance deviation of parameters and data preprocessing method for reusing retired batteries. The proposed method regroups by considering the density and performance deviation of the retired battery dataset through a clustering algorithm using density based spatial clustering of applications with noise (DBSCAN). Additionally, the performance of the algorithm was improved through data preprocessing using a principal component analysis (PCA) that prevents the computational complexity and overfitting of clustering algorithm based on machine learning. The feasibility of the proposed algorithm is verified by comparing to general clustering algorithms.
