Data‐Driven Fast Clustering of Second‐Life Lithium‐Ion Battery: Mechanism and Algorithm. Issue 8 (10th July 2020) - Ran, Aihua; Zhou, Zihao; Chen, Shuxiao; Nie, Pengbo; Qian, Kun; Li, Zhenlong; Li, Baohua; Sun, Hongbin; Kang, Feiyu; Zhang, Xuan; Wei, Guodan
Abstract: 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. Abstract : In order to cluster retired lithium‐ion batteries, a pulse clustering model embedded with an improved bisecting K‐means algorithm is developed, which can effectively cluster batteries from new to the end of life. The test time is reduced from hours to minutes with an accuracy of 88%.