电源学报

基于PCA-CEEMDAN和改进SVR的锂电池RUL预测

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吴伟丽 , 卢双双

电源学报 | 电池与储能 2025,23(6): 267-280

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电源学报 |电池与储能 2025 , 23 (6) : 267 -280

基于PCA-CEEMDAN和改进SVR的锂电池RUL预测

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吴伟丽, 卢双双

作者信息

西安科技大学电气与控制工程学院，西安　710600

通讯作者:

卢双双（1999—　），女，通信作者，硕士研究生。研究方向：锂离子电池剩余使用寿命预测。E-mail：lushuangshuang52@163.com

作者简介:

吴伟丽（1978—　），女，博士，副教授。研究方向：电力系统运行与控制，大电网安全控制与灾变防治。E-mail：wwllxm@163.com。

RUL Prediction of Lithium-ion Batteries Based on PCA-CEEMDAN and Improved SVR

Weili WU, Shuangshuang LU

Affiliations

School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710600, China

出版时间: 2025-10-30 doi: 10.13234/j.issn.2095-2805.2025.6.267

文章导航

摘要

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针对锂离子电池在进行剩余使用寿命RUL（remaining useful life）的直接预测时容量难以获取及间接预测时特征参数存在冗余或不足的问题，提出了1种基于数据预处理技术和改进支持向量回归SVR（support vector regression）的锂离子电池RUL间接预测方法。首先在电池充放电阶段提取多个间接特征参数，利用主成分分析法对众多参数进行去冗余处理，重构出1个包含充分信息的融合健康因子HI（health indicator）；然后搭建基于鲸鱼优化算法和SVR的融合HI预测模型及容量预测模型，利用自适应白噪声完备集成经验模态分解将融合HI分解为若干模态分量，将各分量分别输入融合HI预测模型进行HI预测，将预测结果叠加并输入容量预测模型实现RUL间接预测；最后采用NASA电池退化数据集进行验证。结果表明，所提方法RUL预测结果的平均绝对误差和均方根误差控制在1.78%和2.5%以内，有效提高了RUL的预测精度，为锂离子电池RUL预测提供了新思路。

关键词

锂离子电池 / 剩余使用寿命预测 / 支持向量回归 / 主成分分析 / 经验模态分解

Abstract

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Aimed at the problems that the capacity of a lithium-ion battery is difficult to obtain in the direct prediction of its remaining useful life (RUL) and the characteristic parameters are redundant or insufficient in the indirect prediction, an indirect prediction method for the RUL of lithium-ion battery based on the data preprocessing technology and improved support vector regression (SVR) is proposed. First, multiple indirect characteristic parameters are extracted at the battery charging and discharging stage. Second, principal component analysis is used to remove the redundancy of various parameters, and a fusion health indicator (HI) with sufficient information is reconstructed. Third, a fusion HI prediction model and a capacity prediction model based on the whale optimization algorithm and SVR are built, and the complete ensemble empirical mode decomposition with adaptive noise is used to decompose the fusion HI into several modal components. Each component is input into the fusion HI prediction model for HI prediction, and the prediction results are superimposed into the capacity prediction model to realize indirect RUL prediction. Finally, the NASA battery degradation data set is used for verification, and results show that the mean absolute error and root mean square error of RUL prediction results for the proposed method are controlled within 1.78% and 2.5%, respectively, which effectively improves the prediction accuracy of RUL. The research provides a new idea for the RUL prediction of lithium-ion batteries.

Key words

Lithium-ion battery / remaining useful life (RUL) prediction / support vector regression (SVR) / principal component analysis / empirical mode decomposition

引用本文

吴伟丽, 卢双双. 基于PCA-CEEMDAN和改进SVR的锂电池RUL预测. 电源学报, 2025 , 23 (6) : 267 -280 . DOI: 10.13234/j.issn.2095-2805.2025.6.267

Weili WU, Shuangshuang LU. RUL Prediction of Lithium-ion Batteries Based on PCA-CEEMDAN and Improved SVR[J]. Journal of Power Supply, 2025 , 23 (6) : 267 -280 . DOI: 10.13234/j.issn.2095-2805.2025.6.267

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2025年第23卷第6期

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文章信息

doi: 10.13234/j.issn.2095-2805.2025.6.267

接收时间：2022-11-18
首发时间：2026-10-26
出版时间：2025-10-30

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收稿日期：2022-11-18
修回日期：2023-03-09
录用日期：2023-03-21

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作者信息

西安科技大学电气与控制工程学院，西安　710600

通讯作者:

卢双双（1999—　），女，通信作者，硕士研究生。研究方向：锂离子电池剩余使用寿命预测。E-mail：lushuangshuang52@163.com

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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