Screening of retired batteries with gramian angular difference fields and ConvNeXt
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影响因子：7.802
发表刊物：Engineering Applications of Artificial Intelligence
关键字：Retired batteries, Screening, Piecewise aggregation approximation, Gramian angular difference fields, ConvNeXt
摘要：With the rapid development of electric vehicles, the second usage of retired batteries becomes a key issue. The accuracy of existing screening methods for retired batteries is highly dependent on the feature selection from charging or discharging curves. This paper proposes a novel method of screening retired batteries, in which the constant current (CC) charging curves are converted into images by Gramian angular difference fields (GADF) and classified with a ConvNeXt network. Firstly, the CC charging voltage data is reasonably reduced by piecewise aggregation approximation. Secondly, the CC voltage curves are encoded into images by GADF to make small differences more distinguishable. Then, a ConvNeXt network is used for screening the retired batteries because of its excellent performance on accuracy and scalability. Finally, validation experiments are carried out on 143 retired high-power lithium-ion batteries, and the results show that the proposed screening method has a classification detection accuracy of 93.71%.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：123
页面范围：106397
是否译文：否
发表时间：2023-05-12
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S095219762300581X