CONSISTENCY CLASSIFICATION METHOD AND APPLICATION OF LITHIUM-ION BATTERIES BASED ON EXTERNAL FEATURE IMAGES AND DEEP CONVOLUTIONAL NEURAL NETWORKS

Xia Xiangyang; Zhao Xiaoyue; Yue Jiahui; Wu Jinbo; Peng Cheng; Bo Wenqi

doi:10.19912/j.0254-0096.tynxb.2025-0667

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 28-39. DOI: 10.19912/j.0254-0096.tynxb.2025-0667

CONSISTENCY CLASSIFICATION METHOD AND APPLICATION OF LITHIUM-ION BATTERIES BASED ON EXTERNAL FEATURE IMAGES AND DEEP CONVOLUTIONAL NEURAL NETWORKS

Xia Xiangyang¹, Zhao Xiaoyue¹, Yue Jiahui¹, Wu Jinbo², Peng Cheng³, Bo Wenqi⁴

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Abstract

Inconsistency problems in energy storage stations are often resolved by adjusting the voltage difference of battery packs with external offline devices, while relevant solutions remain limited. This paper proposes an energy storage lithium-ion battery consistency classification method combining external feature images with the Transfer Learning （TL）-GoogLeNet convolutional neural network （CNN） model. The method constructs external feature images from battery voltage, temperature, and IC curves. It utilizes grayscale layers to highlight peaks and inflection points of curves via different brightness regions, which improves the effective representation of battery status and captures key changes during the aging process. The transfer learning strategy transfers parameters of the pre-trained GoogLeNet to target battery datasets to build the TL-GoogLeNet model for better generalization performance. Comprehensive validation is performed using both publicly available lithium battery datasets and practical operating data from energy storage stations. The proposed method achieves an accuracy rate of over 95%, demonstrating its high classification accuracy and robustness. The results suggest that this method can provide effective guidance for inconsistency elimination and safe maintenance of energy storage stations during their later stages of operation.

Key words

lithium-ion battery / consistency / Gramian angular field / GoogLeNet model / transfer learning / external feature images

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Xia Xiangyang, Zhao Xiaoyue, Yue Jiahui, Wu Jinbo, Peng Cheng, Bo Wenqi. CONSISTENCY CLASSIFICATION METHOD AND APPLICATION OF LITHIUM-ION BATTERIES BASED ON EXTERNAL FEATURE IMAGES AND DEEP CONVOLUTIONAL NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 28-39 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0667

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