Fast Clustering of Retired Lithium-Ion Batteries for Secondary Life with a Two-Step Learning Method

Abstract

Secondary utilization of retired lithium-ion batteries (LIBs) from electric vehicles could provide significant economic benefits. Herein, based on a short pulse test, we propose a two-step machine leaning method, which combines unsupervised K-means clustering and supervised Gaussian process regression for sorting and estimating the remaining capacity of retired LIBs simultaneously. First, the pulse test to reflect battery aging is detailed, and the significance of the screening process in clustering batteries is validated by the poor clustering accuracy of over 500 unscreened batteries and the various thermal performance of six types of batteries. However, unsupervised K-means can sort out the same type of batteries, which is further verified by the Gaussian mixture model. Furthermore, the remaining capacity of various types of LIBs is given by supervised Gaussian process regression with a correlation coefficient of over 98%. Finally, an automatic sorting machine is designed to corporate with the fast-clustering method, improving the sorting efficiency of retired LIBs.

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

“…101 Furthermore, by means of low-timecost pulse tests and extracting features from pulse data, the used LIBs have been fast-screened and efficiently clustered into different clusters according to their electrochemical performance through target-free unsupervised learning. [102][103][104]…”

“…Taking advantage of ML, the distribution of the partial charging voltage curve of used LIBs is markedly correlated to their remaining capacity, thus reducing the testing time 101 . Furthermore, by means of low‐time–cost pulse tests and extracting features from pulse data, the used LIBs have been fast‐screened and efficiently clustered into different clusters according to their electrochemical performance through target‐free unsupervised learning 102–104 …”

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

“…EU government has supported a big research program called "Battery 2030" with EUR 40.5 million, where battery health management and prognostic algorithms are important components 20 . In addition, battery reuse and recycling policies pronounced by governments from Europe and China also indicate the importance of battery health prognostics 19,21 , which promotes the research on this topic where the health evaluations are significant steps to point out the health status of batteries 22,23 .…”

“…20 In addition, battery reuse and recycling policies pronounced by governments from Europe and China also indicate the importance of battery health prognostics, 19,21 which promotes the research on this topic where health evaluations are significant steps to point out the health status of batteries. 22,23…”

“…20 In addition, battery reuse and recycling policies pronounced by governments from Europe and China also indicate the importance of battery health prognostics, 19,21 which promotes the research on this topic where health evaluations are significant steps to point out the health status of batteries. 22,23 Aging and damage of lithium-ion batteries can lead to battery system failure, even property damage and personal injury. 24,25 Batteries undergo degradation because complex and coupled side reactions occur along with the main electrochemical reactions during charging and discharging.…”