基于外特征图像与深度卷积神经网络的锂离子电池一致性分类方法及应用

夏向阳; 赵晓悦; 岳家辉; 吴晋波; 彭铖; 柏文琦

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

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基于外特征图像与深度卷积神经网络的锂离子电池一致性分类方法及应用

更新时间：2026-10-15

- 基于外特征图像与深度卷积神经网络的锂离子电池一致性分类方法及应用
- Vol. 47, Issue 9, Pages: 28-39(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0667
  CLC： TM912
- Online First：10 October 2026，
  
  Published：2026
- 稿件说明：
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夏向阳, 赵晓悦, 岳家辉, et al. 基于外特征图像与深度卷积神经网络的锂离子电池一致性分类方法及应用[J]. 2026, 47(9): 28-39.
DOI：

夏向阳, 赵晓悦, 岳家辉, et al. 基于外特征图像与深度卷积神经网络的锂离子电池一致性分类方法及应用[J]. 2026, 47(9): 28-39. DOI： 10.19912/j.0254-0096.tynxb.2025-0667.

摘要

针对储能电站电池一致性改善手段较为单一、主要依赖外置离线设备调节电池箱间电压差的问题

提出一种将外特征图像与TL-GoogLeNet卷积神经网络模型相结合的储能电站锂离子电池一致性分类方法。该方法以电池电压、温度及IC曲线构建外特征图像

利用灰度层突显曲线峰值与拐点特征

以明暗区域强化特征表现

增强电池状态的有效表征并捕捉其老化过程中的关键变化。采用迁移学习策略将预训练GoogLeNet模型参数迁移至特定电池数据集

构建TL-GoogLeNet模型以提升模型泛化性能。通过锂电池公开数据集和实际储能站运行数据进行综合验证

所提方法准确率均保持在95%以上

结果表明该方法具有较高的分类准确性和鲁棒性

可为储能电站运行后期的不一致性问题和运行安全维护提供有效指导。

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.

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references

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