Estimation of Lithium-Ion Battery State of Health Using Global Health Factor and Residual Model

HU Xunquan; GENG Limin; SHU Junhao; ZHANG Wenbo; WU Chunling; WEI Xiaolong; HUANG Dong; CHEN Hao

doi:10.7652/xjtuxb202504010

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Estimation of Lithium-Ion Battery State of Health Using Global Health Factor and Residual Model

更新时间：2026-01-06

- Estimation of Lithium-Ion Battery State of Health Using Global Health Factor and Residual Model
- Journal of Xi’an Jiaotong University Vol. 59, Issue 4, Pages: 105-117(2025)
- 作者机构：
  
  1.长安大学陕西省交通新能源开发、应用与汽车节能重点实验室， 710064，西安
  2.长安大学西安市交通先进动力重点实验室， 710064，西安
  3.陕西重型汽车有限公司汽车工程研究院， 710200，西安
  4.核工业二○三研究所， 712099，陕西咸阳
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202504010
  CLC： TM912.8
- Received：29 November 2024，
  
  Online First：02 January 2025，
  
  Published：10 April 2025
- 稿件说明：
移动端阅览
HU Xunquan, GENG Limin, SHU Junhao, et al. Estimation of Lithium-Ion Battery State of Health Using Global Health Factor and Residual Model[J]. Journal of Xi’an Jiaotong University, 2025, 59(4): 105-117.
DOI：

HU Xunquan, GENG Limin, SHU Junhao, et al. Estimation of Lithium-Ion Battery State of Health Using Global Health Factor and Residual Model[J]. Journal of Xi’an Jiaotong University, 2025, 59(4): 105-117. DOI： 10.7652/xjtuxb202504010.

摘要

为准确估计锂离子电池的健康状态（SOH），提出了一种卷积神经网络-残差网络-双向门控循环单元-注意力机制（CNN-Residual-BiGRU-Attention）模型和微调估计方法。首先，采用分段近似聚合算法对电池容量增量和恒流充电曲线进行降维，构建全局健康因子；接着，利用卷积神经网络提取全局健康因子时序特征，通过注意力机制突出强相关特征，并引入残差网络保持信息完整性；最后，通过改进人工蜂群算法对模型超参数寻优，提升模型SOH估计精度。采用美国国家航空航天局和牛津大学锂离子电池数据集进行精度验证，结果表明：利用提出的微调估计方法，即使精度较差的卷积神经-长短期记忆模型，SOH估计结果的平均绝对误差

MAE

、平均绝对百分比误差

MAPE

和均方根误差

RMSE

也均在2%以内；相较于卷积神经网络-双向门控循环单元-注意力机制模型，采用CNN-Residual-BiGRU-Attention模型对训练集比例为30%的同一电池SOH进行估计，得到的

MAE

、

MAPE

和

RMSE

分别降低了41.86%、44.35%、42.11%；对训练集比例为40%的同类电池SOH进行估计，得到的

MAE

、

MAPE

和

RMSE

分别降低了45.51%、45.93%、40.10%。该研究结果可为低比例训练集条件下准确估计锂离子电池的SOH提供理论参考。

Abstract

To accurately estimate the state of health (SOH) of lithium-ion batteries

a convolutional neural network-residual-BiGRU-attention (CNN-Residual-BiGRU-Attention) model and fine-tuning estimation method are proposed. Firstly

a piecewise approximate aggregation algorithm is used to reduce the dimensionality of battery capacity increment and constant current charging curves to construct a global health facto

r. Secondly

the CNN is used to extract the temporal features of the global health factor

highlighting strong correlations through an attention mechanism

and the residual network is introduced to maintain information integrity. Finally

by improving the artificial bee colony algorithm for model hyperparameter optimization

the accuracy of SOH estimation is enhanced. The accuracy is validated using lithium-ion battery datasets from NASA and the University of Oxford. The results show that

even with a less accurate convolutional neural network-long short-term memory model

the proposed fine-tuning estimation method keeps the mean absolute error

MAE

mean absolute percentage error

MAPE

and root mean square error

RMSE

of the SOH estimation results within 2%. Compared with the CNN-BiGRU-Attention model

when the CNN-Residual-BiGRU-Attention model is employed to estimate the SOH of the same battery with a training set ratio of 30%

the

MAE

、

MAPE

and

RMSE

of the estimated results are reduced by 41.86%

44.35% and 42.11%

respectively. For estimating the SOH of the same type of batteries with a training set ratio of 40%

the

MAE

、

MAPE

and

RMSE

of the estimated results are reduced by 45.51%

45.93%

and 40.10%

respectively. These research findings provide a theoretical reference for accurately estimating the SOH of lithium-ion batteries under low training set conditions.

关键词

Keywords

references

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