基于特征重构与多时间尺度的锂电池SOH和RUL联合估计

寇发荣; 杨天祥; 罗希; 王衎; 周东明

doi:doi:10.19912/j.0254-0096.tynxb.2024-0382

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基于特征重构与多时间尺度的锂电池SOH和RUL联合估计

更新时间：2025-12-02

- 基于特征重构与多时间尺度的锂电池SOH和RUL联合估计
- Vol. 46, Issue 6, Pages: 68-78(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0382
  CLC： TM912
- Published Online：07 July 2025，
  
  Published：2025
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寇发荣, 杨天祥, 罗希, et al. 基于特征重构与多时间尺度的锂电池SOH和RUL联合估计[J]. 2025, 46(6): 68-78.
DOI：

寇发荣, 杨天祥, 罗希, et al. 基于特征重构与多时间尺度的锂电池SOH和RUL联合估计[J]. 2025, 46(6): 68-78. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0382.

摘要

提出一种基于特征重构与多时间尺度的健康状态（SOH）和剩余寿命（RUL）联合估计方法。首先

从锂电池老化数据集中提取健康特征

使用变分模态分解算法重构特征数据;在此基础上

利用贝叶斯算法优化卷积长短期记忆网络构建SOH估计模型

通过微观尺度下的算法迭代与宏观尺度下的卷积神经网络模型相结合来实现SOH与RUL准确且高效的估计。利用美国国家航空航天局（NASA）和马里兰大学（CALCE）公开数据集进行验证

结果显示：SOH的估计误差稳定保持在1%以内

估计精度较特征重构前提升约35%;RUL预测结果的平均绝对误差（MAE）和均方根误差（RMSE）分别保持在0.42和0.78以内

实现了SOH和RUL的高精度估计。

Abstract

A joint estimation method of SOH and RUL based on feature reconstruction and multiple time scales is proposed in this paper. Firstly

six health features related to capacity degradation are extracted from the lithium battery aging data set

and the feature data is reconstructed by using variational mode decomposition algorithm. The feature reconstruction was achieved by filtering the participating modes through user-defined indicators; On this basis

the Bayesian algorithm is used to optimize the convolutional long-term and short-term memory network to construct the SOH estimation model. The accurate and efficient estimation of SOH and RUL is achieved by combining the algorithm iteration at the micro scale with the convolutional neural network model at the macro scale. The results show that the estimation error of SOH is stable within 1%

and the estimation accuracy is about 35% higher than that before feature reconstruction; The mean absolute error （MAE） and root mean square error（RMSE） of RUL prediction results are kept within 0.42 and 0.78

respectively

which realizes the high-precision estimation of SOH and RUL.

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references

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