支持向量机
电池组
重新使用
一致性(知识库)
核(代数)
电池(电)
电池容量
计算机科学
班级(哲学)
功能(生物学)
电压
径向基函数核
人工智能
工程类
模式识别(心理学)
机器学习
核方法
电气工程
数学
功率(物理)
物理
量子力学
组合数学
进化生物学
生物
废物管理
作者
Hao Qiang,Yuanlin Liu,Wanjie Zhang
出处
期刊:Journal of electrochemical energy conversion and storage
[ASME International]
日期:2023-08-09
卷期号:21 (2)
摘要
Abstract With the retirement of a large number of lithium-ion batteries from electric vehicles, their reuse has received increasing attention. However, a retired battery pack is not suitable for direct reuse due to the poor consistency of in-pack batteries. This paper proposes a method of retired lithium-ion battery screening based on support vector machine (SVM) with a multi-class kernel function. First, ten new NCR18650B batteries were used to carry out the aging experiments for collecting the main parameters, such as capacity, voltage, and direct current resistance. Second, an SVM based on a multi-class kernel function was proposed to screen retired batteries. To improve the screening efficiency, a capacity/voltage second-order conductance curve was adopted to extract their capacity features quickly, and four new feature points were selected as the input of the SVM to classify retired batteries. Finally, the retired batteries are accurately divided into four classes by the trained model, and the classification accuracy can reach 97.0%. Compared with the traditional method, the feature extraction time can be reduced by four-fifths, and the screening efficiency is greatly improved.
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