A semiparametric clustering method for the screening of retired Li-ion batteries from electric vehicles

Abstract

This study proposes a semiparametric clustering method (SPCM) to screen the batteries retired from electric vehicles (EVs) for echelon utilization. To quickly capture the static and dynamic battery characteristics, a hybrid test combining the low-current hybrid pulse power characterization (L-HPPC) test and China light-duty vehicle test cycle (CLTC) is designed. The pseudo-two-dimensional (P2D) model is combined with genetic algorithm (GA), and model parameters are identified by the hybrid test. Then, a clustering matrix containing sensitive model parameters is established and processed by principal component analysis (PCA). In SPCM, the Euclidean-distance-based clustering matrix is divided into high-density and low-density parts through a predetermined mixing proportion. For high-density data, hierarchical clustering (HC) is used to determine the initial cluster centers, and fuzzy C-means (FCM) method is utilized to determine the core clusters, while the low-density data is assigned to the nearest neighbours. Finally, 18 open-access datasets are employed to validate the effectiveness of the proposed SPCM. These results indicate that the proposed SPCM is superior to K-mean, K-medoid, and FCM in terms of normalized mutual information (NMI). Furthermore, the pulse testing and capacity testing of the re-grouped retired batteries further demonstrate the effectiveness of the SPCM in the screening of retired batteries.