一致性(知识库)
电池(电)
一般化
电压
算法
电池容量
方案(数学)
计算机科学
锂(药物)
随机森林
锂离子电池
电气工程
数学
工程类
人工智能
功率(物理)
物理
数学分析
医学
量子力学
内分泌学
作者
Peng Huang,Ying Zhang,Yongzhe Kang,Pingwei Gu,Bin Duan,Chenghui Zhang
出处
期刊:IEEE Transactions on Transportation Electrification
日期:2024-04-24
卷期号:11 (1): 544-557
被引量:4
标识
DOI:10.1109/tte.2024.3392925
摘要
with the vast lithium-ion batteries are disassembled from the electric vehicles, the screening and regrouping of retired batteries are increasingly crucial. Currently, known screening methods such as direct measurement methods for retired batteries exist the problem of low efficiency due to the long test time, and Indirect methods lack generalization because of a single dataset. In this paper, a flexible screening scheme for two mainstream types of retired batteries based on random forest (RF) algorithm and new feature is proposed. Firstly, retired battery modules are disassembled into battery cells. Subsequently, the incremental capacity (IC) curves of all batteries are gained by differentiating the capacity-voltage curves, and new curves, namely incremental IC (IIC) curves are acquired by calculating the numerical differentiation of IC curves. Secondly, two RF models are designed with LiNCM and LiFePO 4 batteries data, respectively. The peak coordinates of IC and IIC curves are taken as inputs to the model. Finally, a voltage test system is built to evaluates consistency among the retired batteries. The experiment results of 108 samples show that overall screening accuracy reaches 97.2%. In addition, the voltage consistency after screening is improved than before, and the maximum standard deviation drops by up to 14 times.
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