Screening of retired batteries with gramian angular difference fields and ConvNeXt

Abstract

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%.