分类
锂(药物)
聚类分析
分类
过程(计算)
电池(电)
k均值聚类
支持向量机
高斯过程
模式识别(心理学)
机器学习
高斯分布
计算机科学
工程类
化学
人工智能
算法
医学
功率(物理)
物理
计算化学
量子力学
情报检索
内分泌学
操作系统
作者
Aihua Ran,Zheng Liang,Shuxiao Chen,Ming Cheng,Chongbo Sun,Feiyue Ma,Li Wang,Baohua Li,Guangmin Zhou,Xuan Zhang,Feiyu Kang,Guodan Wei
出处
期刊:ACS energy letters
[American Chemical Society]
日期:2022-10-11
卷期号:7 (11): 3817-3825
被引量:17
标识
DOI:10.1021/acsenergylett.2c01898
摘要
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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