Associate professor
Supervisor of Master's Candidates
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Journal:Engineering Applications of Artificial Intelligence
Key Words:Retired batteries, Screening, Piecewise aggregation approximation, Gramian angular difference fields, 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%.
Indexed by:Journal paper
Discipline:Engineering
Document Type:J
Volume:123
Page Number:106397
Translation or Not:no
Date of Publication:2023-05-12
Included Journals:SCI
Links to published journals:https://www.sciencedirect.com/science/article/pii/S095219762300581X
