Research on anomaly and fault defect identification in power equipment based on multimodal large models

Shuang Lin, Wenxu Yao, Yuluan Liu, Yan Yang and Jiali Xiong

International Journal of Low-Carbon Technologies, 2026, vol. 21, issue 1, 1c-16

Abstract: This enables the analysis and judgment of the aging degree and consistency of cells within clusters. Meanwhile, neural networks are used to predict the entropy values for short-term health state forecasting of the energy storage station. Finally, the feasibility and effectiveness of the feature data information entropy method for health state assessment and prediction are validated using actual operational data of the energy storage station and a 20S1P battery simulation model. This paper is the first to introduce information entropy theory into the health status assessment of lithium-ion energy storage stations.

Keywords: lithium-ion batteries; battery clusters; information entropy; characterization data; constant current discharge (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:21:y:2026:i:1:p:1c-16.

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