Estimation of lithium-ion battery state of health based on random charging segments and DBFN-ML

ZHANG Yu; ZHAO Zhiheng; ZHOU Hairui

doi:10.19799/j.cnki.2095-4239.2025.1021

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Estimation of lithium-ion battery state of health based on random charging segments and DBFN-ML

Energy Storage Test\: Methods and Evaluation | 更新时间：2026-07-13

- Estimation of lithium-ion battery state of health based on random charging segments and DBFN-ML
- Energy Storage Science and Technology Vol. 15, Issue 6, Pages: 2368-2379(2026)
- 作者机构：
  
  哈尔滨工业大学电气工程及自动化学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.1021
  CLC： TM 912
- Received：12 November 2025，
  
  Revised：2026-01-04，
  
  Published：28 June 2026
- 稿件说明：
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张钰, 赵志衡, 周海睿. 基于随机充电片段和DBFN-ML的锂电池健康状态估计[J]. 储能科学与技术, 2026, 15(6): 2368-2379.

ZHANG Yu, ZHAO Zhiheng, ZHOU Hairui. Estimation of lithium-ion battery state of health based on random charging segments and DBFN-ML[J]. Energy Storage Science and Technology, 2026, 15(6): 2368-2379.
张钰, 赵志衡, 周海睿. 基于随机充电片段和DBFN-ML的锂电池健康状态估计[J]. 储能科学与技术, 2026, 15(6): 2368-2379. DOI： 10.19799/j.cnki.2095-4239.2025.1021.

ZHANG Yu, ZHAO Zhiheng, ZHOU Hairui. Estimation of lithium-ion battery state of health based on random charging segments and DBFN-ML[J]. Energy Storage Science and Technology, 2026, 15(6): 2368-2379. DOI： 10.19799/j.cnki.2095-4239.2025.1021.

摘要

准确估计锂电池健康状态（state of health，SOH）对保障储能系统的稳定运行至关重要。在实际应用场景中，电池充电过程通常呈现非完整与非固定特性，导致可获取的充电数据多为随机片段，使得传统SOH估计方法难以有效应对。针对该问题，本研究提出一种基于双分支融合网络（dual-branch fusion network，DBFN）与元学习（meta-learning，ML）的SOH估计方法。首先，将原始充电数据划分为多个电压区间片段，从中提取增量容量（incremental capacity，IC）平均值、充电容量及充电时间作为健康特征（health feature，HF），并将初始与结束电压作为片段位置信息特征。其次，构建DBFN模型，通过2个独立分支分别处理上述2类特征，以捕捉与电池退化相关的健康信息及片段上下文位置信息，并借助融合模块实现基于随机充电片段的SOH估计。进一步引入元学习，利用少量样本进行多电池任务学习，从而快速优化DBFN参数并增强模型泛化能力。最后，基于美国国家航空航天局（National Aeronautics and Space Administration，NASA）锂电池数据集进行实验验证，将本研究模型与其他模型进行对比。实验结果表明，所提方法在SOH估计方面表现优异，最优均方误差为0.00012，平均绝对误差为0.00892，平均绝对百分比误差为1.14%。

Abstract

Accurate estimation of the state of health (SOH) lithium batteries is crucial for ensuring the stable operation of energy-storage systems. In practical applications

the battery-charging process often exhibits incomplete and non-stationary characteristics

resulting in predominantly random segments of charging data. This poses significant challenges to traditional SOH-estimation methods. To address this issue

in this paper we propose an SOH-estimation method based on a dual-branch fusion network (DBFN) and meta learning (ML). First

we divide the raw charging data into multiple voltage-interval segments

and we extract from them the average incremental capacity

charging capacity

and charging time as health features. We utilize the initial and terminal voltages of each segment as positional-information features. Subsequently

we construct a DBFN model that includes two independent branches designed to process the aforementioned two types of features. This approach captures both the health-related information associated with battery degradation and the contextual positional information from the segments. We then employ a fusion module to estimate the SOH based on the random charging segments. Furthermore

we introduce ML to facilitate multi-battery task learning with limited samples

thereby rapidly optimizing the DBFN parameters and enhancing the capability for generalizing the model. Finally

we performed experimental validation using the NASA lithium-battery dataset and compared the proposed model with other benchmark models. Our results demonstrate that the proposed method excels in SOH estimation

achieving an optimal mean square error of 0.00012

a mean absolute error of 0.00892

and a mean absolute percentage error of 1.14%.

关键词

Keywords

references

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