A semiparametric clustering method for the screening of retired Li-ion batteries from electric vehicles. (July 2023)

• Record Type: Journal Article
• Authors: Lyu, Zhiqiang; Zhang, Yunfei; Wang, Geng; Gao, Renjing
• Is Part Of: Journal of energy storage. Volume 63(2023)
• Publication Date: 2023-07
• Subjects: Retired Li-ion batteries -- Semiparametric clustering method -- Clustering method -- Principal component analysis -- Genetic algorithm -- P2D model; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2023.107030
• Languages: English
• ISSNs: 2352-152X

Abstract

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. Highlights: A hybrid test for fast battery clustering is designed. The P2D model is utilized to extract the clustering features. A SPCM based on parametric clustering and nonparametric clustering is proposed. The effectiveness of proposed SPCM is verified by the public datasets. The pulse test and capacity test are used to verify the SPCM for retired batteries.