EN基于可解释Shapelets的锂离子电池健康状态估计
基于可解释Shapelets的锂离子电池健康状态估计
- 太阳能学报 2025年46卷第12期 页码：76-84
- 作者机构：
- 作者简介：
- 基金信息：国家自然科学基金（52307242）;中央高校基本科研业务费专项资金（2023JC001）
DOI：doi:10.19912/j.0254-0096.tynxb.2024-1209
中图分类号： TM73-
网络出版：2026-01-07，
纸质出版：2025
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李沂洹, 郑涵晋, 王玮, 等. 基于可解释Shapelets的锂离子电池健康状态估计[J]. 太阳能学报, 2025,46(12):76-84.
李沂洹, 郑涵晋, 王玮, et al. 基于可解释Shapelets的锂离子电池健康状态估计[J]. 2025, 46(12): 76-84.
李沂洹, 郑涵晋, 王玮, 等. 基于可解释Shapelets的锂离子电池健康状态估计[J]. 太阳能学报, 2025,46(12):76-84. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1209.
李沂洹, 郑涵晋, 王玮, et al. 基于可解释Shapelets的锂离子电池健康状态估计[J]. 2025, 46(12): 76-84. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1209.
摘要
针对基于机器学习的锂离子电池电池健康状态（SOH）估计可解释性差、对数据的依赖性强等问题
提出一种具有可解释性的锂电池SOH估计方法。首先
研究分析随着电池老化
充电电压与首次充电电压之间的距离呈现出较好的趋势性
基于Shapelets概念提出能够捕捉电池退化趋势的距离度量
进一步通过相关性分析确定Shapelets候选集范围以提高特征提取效率
结合减法平均优化算法（SABO）的BP模型
进行Shapelets的选择。最后
设计基于Shapelets的SABO-BP模型
实现电池SOH的有效估计。在斯坦福大学与麻省理工学院（Stanford-MIT）提供的数据集上对所提方法进行验证
通过使用不同充电策略的电池进行测试
得到电池SOH估计的平均绝对误差（MAE）均维持在0.5%以内
最低可达0.19%;均方根误差（RMSE）也均保持在0.6%以内
最低可达0.26%;决定系数R2保持在0.98以上
最高可达0.995。实验结果表明
所提方法能在数据有限的情况下准确预测锂电池SOH
证实了所提算法的泛化性与实用价值。
Abstract
In response to the problems of poor interpretability and strong dependence on data for battery state of health（SOH） estimation of lithium-ion batteries based on machine learning
this paper proposes a SOH estimation method for lithium batteries with interpretability. First of all
this work analyzes that as batteries age
the distance between the charging voltage and the first charging voltage exhibits a good trend
and proposes a distance metric based on the concept of Shapelets that can capture the degradation trend of the battery
and further determines the range of the Shapelets candidate set through correlation analysis to improve the efficiency of the feature extraction. The selection of Shapelets is carried out in combination with the BP model optimized by the subtraction-average-based optimizer (SABO) algorithm. Finally
the Shapelets-based SABO-BP model is designed to realize the effective estimation of battery SOH. The proposed method is validated on the Stanford-MIT dataset
and batteries with different charging strategies are selected for test
the MAE of the battery SOH estimation are all maintained within 0.5%
and can reach as low as 0.19%; the RMSE are also all maintained within 0.6%
and can reach as low as 0.26%; and the stays above 0.98 and reaches up to 0.995. The experimental results show that the proposed method is able to accurately predict the SOH of lithium batteries with limited data
which confirms the generalization and practical value of the proposed algorithm.
关键词
Keywords
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