A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries. (June 2020)

• Record Type: Journal Article
• Authors: Xu, Zhicheng; Wang, Jun; Lund, Peter D.; Fan, Qi; Dong, Ting; Liang, Yan; Hong, Jie
• Is Part Of: Journal of energy storage. Volume 29(2020)
• Publication Date: 2020-06
• Subjects: Retired Li-ion batteries -- Battery grouping -- Clustering algorithm -- Davies-Bouldin Index; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2020.101303
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: A complete process for grouping retired batteries is proposed including safety checking, performance evaluation, data processing, and clustering of batteries. A novel clustering algorithm of retired batteries based on traversal optimization is proposed. The proposed algorithm shows that the greatest differences are found between clusters, but the least differences between the samples within a single cluster, which indicates the effectiveness of the algorithm. Abstract: The rapid deployment of lithium-ion batteries in clean energy and electric vehicle applications will also increase the volume of retired batteries in the coming years. Retired Li-ion batteries could have residual capacities up to 70–80% of the nominal capacity of a new battery, which could be lucrative for a second-life battery market, also creating environmental and economic benefits. Presently, retired batteries are first screened to select usable batteries and then a proper secondary application is choosen according to the battery performance. Here, a complete process for grouping used batteries is proposed including safety checking, performance evaluation, data processing, and clustering of batteries. Also, a novel clustering algorithm of retired batteries based on traversal optimization is proposed. The new method does not require defining the cluster numbers and centers in beforehand, but possesses immunity to outliers. It can be used both for small and large sample sizes, as the optimization parameters used do not require iteration. The Davies-Bouldin Index of the proposed algorithm shows that the greatest differences are found between clusters, but the least differences between the samples within a single cluster, which indicates the effectiveness of the algorithm. Graphical abstract: Image, graphical abstract