EN基于特征重构与多时间尺度的锂电池SOH和RUL联合估计
基于特征重构与多时间尺度的锂电池SOH和RUL联合估计
- 太阳能学报 2025年46卷第6期 页码：68-78
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DOI：doi:10.19912/j.0254-0096.tynxb.2024-0382
中图分类号： TM912-
网络出版：2025-07-07，
纸质出版：2025
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寇发荣, 杨天祥, 罗希, 等. 基于特征重构与多时间尺度的锂电池SOH和RUL联合估计[J]. 太阳能学报, 2025,46(6):68-78.
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.
寇发荣, 杨天祥, 罗希, 等. 基于特征重构与多时间尺度的锂电池SOH和RUL联合估计[J]. 太阳能学报, 2025,46(6):68-78. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0382.
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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Keywords
references
李建林, 屈树慷, 马速良, 等. 电池储能系统辅助电网调频控制策略研究[J]. 太阳能学报, 2023, 44(3): 326-335.
夏向阳, 陈贵全, 夏天, 等. 二次谐波分量下锂离子电池安全运行区间研究[J]. 太阳能学报, 2023, 44(10): 496-503.
YANG R X, XIONG R, HE H W, et al.A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application[J]. Journal of cleaner production, 2018, 187: 950-959.
CHEN Z W, SHI N, JI Y F, et al.Lithium-ion batteries remaining useful life prediction based on BLS-RVM[J]. Energy, 2021, 234: 121269.
TIAN H X, QIN P L, LI K, et al.A review of the state of health for lithium-ion batteries: research status and suggestions[J]. Journal of cleaner production, 2020, 261: 120813.
LI C F, YANG L, LI Q, et al.SOH estimation method for lithium-ion batteries based on an improved equivalent circuit model via electrochemical impedance spectroscopy[J]. Journal of energy storage, 2024, 86: 111167.
HOSSEININASAB S, LIN C W, PISCHINGER S, et al.State-of-health estimation of lithium-ion batteries for electrified vehicles using a reduced-order electrochemical model[J]. Journal of energy storage, 2022, 52: 104684.
KHODADADI S K, JIN X, RIZZONI G.Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health[J]. Journal of power sources, 2021, 481: 228861.
LI Y, LIU K L, FOLEY A M, et al.Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review[J]. Renewable and sustainable energy reviews, 2019, 113: 109254.
李练兵, 朱乐, 李思佳, 等. 基于差分电压和ICS-Elman神经网络的锂离子电池剩余使用寿命预测方法[J]. 太阳能学报, 2023, 44(12): 433-443.
ZHAO L, WANG Y P, CHENG J H.A hybrid method for remaining useful life estimation of lithium-ion battery with regeneration phenomena[J]. Applied sciences, 2019, 9(9): 1890.
ZHANG J A, WANG P, GONG Q R, et al.SOH estimation of lithium-ion batteries based on least squares support vector machine error compensation model[J]. Journal of power electronics, 2021, 21(11): 1712-1723.
GONG Y D, ZHANG X Y, GAO D Z, et al.State-of-health estimation of lithium-ion batteries based on improved long short-term memory algorithm[J]. Journal of energy storage, 2022, 53: 105046.
何冰琛, 杨薛明, 王劲松, 等. 基于PCA-GPR的锂离子电池剩余使用寿命预测[J]. 太阳能学报, 2022, 43(5): 484-491.
TOUGHZAOUI Y, TOOSI S B, CHAOUI H, et al.State of health estimation and remaining useful life assessment of lithium-ion batteries: a comparative study[J]. Journal of energy storage, 2022, 51: 104520.
王萍, 范凌峰, 程泽. 基于健康特征参数的锂离子电池SOH和RUL联合估计方法[J]. 中国电机工程学报, 2022, 42(4): 1523-1534.
GU J X, WANG Z H, KUEN J, et al.Recent advances in convolutional neural networks[J]. Pattern recognition, 2018, 77: 354-377.
孙原理, 宋志浩. 基于卷积长短期记忆网络和人工鲸鱼算法的核反应堆运行事件诊断方法研究[J]. 核动力工程, 2022, 43(4): 185-190.
BIAU G, SCORNET E.A random forest guided tour[J]. TEST, 2016, 25(2): 197-227.
BELGIU M, DRAGUT L.Random forest in remote sensing: a review of applications and future directions[J]. ISPRS journal of photogrammetry and remote sensing, 2016, 114: 24-31.
PELIKAN M, GOLDBERG D E, TSUTSUI S.Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms[C]//SICE 2003 Annual Conference. Fukui, Japan, 2003: 2738-2743.
DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
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