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The commonly used polycrystalline Ni-rich LiNi0.8Co0.1Mn0.1O2 (NCM811) cathode materials suffer from the electrochemical degradation such as rapid impedance growth and capacity decay due to their intrinsically vulnerable grain-boundary fracture during...

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.

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While electrical vehicles (EVs) are expanding rapidly and getting more and more popular in the market, researchers have started to leverage the remaining capacity of used or to‐be‐retired batteries for their second‐life applications. It is crucial to develop a fast and efficient technology to first sort them and then extend their life while delivering energy, waste reduction, and economic benefits. In this work, a pulse clustering model embedded with improved bisecting K‐means algorithm is developed to effectively sort retired batteries with life cycles ranging from new to an end‐of‐life state. The relevance of selected variables is rigorously validated, reaching the accuracy as high as 88% compared with the traditional full charge–discharge test. To note, the test time has largely reduced from hours to minutes. This data‐driven clustering modeling with fast pulse test is a promising approach for clustering lithium‐ion batteries, which is demonstrated with a home‐built and high throughput intelligent clustering machine. In general, the technology opens a new generation of battery clustering, improving the efficiency and accuracy over the past semiempirical approaches.

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