电源学报

基于ER-ELM模型的锂离子电池剩余使用寿命预测

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聂灶金 ¹ , 何晓霞 ¹^,² , 吴传菊 ¹

电源学报 | 人工智能与电力电子 2026,24(3): 249-258

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电源学报 |人工智能与电力电子 2026 , 24 (3) : 249 -258

基于ER-ELM模型的锂离子电池剩余使用寿命预测

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聂灶金¹, 何晓霞¹^,², 吴传菊¹

作者信息

^1.武汉科技大学理学院，武汉　430065

^2.武汉科技大学冶金工业过程系统科学湖北省重点实验室，武汉　430081

通讯作者:

何晓霞（1979—　），女，通信作者，博士，教授。研究方向：高维数据的统计推断。E-mail：hexiaoxia@wust.edu.cn。

作者简介:

聂灶金（1999—　），女，硕士研究生。研究方向：工业大数据分析。E-mail：niezaojin@wust.edu.cn。

吴传菊（1974—　），女，博士，副教授。研究方向：应用数理统计。E-mail：wuchuanju@wust.edu.cn。

Remaining Useful Life Prediction for Lithium-ion Batteries Based on ER-ELM Model

Zaojin NIE¹, Xiaoxia HE¹^,², Chuanju WU¹

Affiliations

^1.College of Science, Wuhan University of Science and Technology, Wuhan 430065, China

^2.Hubei Key Laboratory for Systems Science in Metallurgical Processes, Wuhan University of Science and Technology, Wuhan 430081, China

出版时间: 2026-03-30 doi: 10.13234/j.issn.2095-2805.2026.3.249

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摘要

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容量和阻抗通常是用于确定锂离子电池健康状态的直接特征，但不易直接测量。针对锂离子电池剩余使用寿命RUL（remaining useful life）预测的问题，提出1种基于期望分位数回归ER（expectile regression）的间接预测方法。首先，从电池的充电过程中提取能够表征电池性能退化的间接健康因子，并利用Pearson和Spearman相关性分析法分析其与容量之间的相关性；其次，通过结合极限学习机ELM（extreme machine learning），开发了1种新的期望分位数回归-极限学习机（ER-ELM）模型；引入BFGS拟牛顿法优化ER-ELM模型参数，建立锂离子电池RUL预测模型；最后，利用CALCE锂电池数据集进行模型验证。结果表明，与标准ELM模型相比，ER-ELM模型预测结果输出稳定，预测结果的最大均方根误差为0.028 9，拟合优度均在98%以上，在追踪容量衰退和再生方面具有更好的性能。

关键词

锂离子电池 / 剩余使用寿命 / 期望分位数回归 / 极限学习机 / 拟牛顿优化算法

Abstract

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Capacity and impedance are usually used as direct characteristics to determine the health status of lithium-ion batteries, but they cannot be easily measured in a direct manner. Aimed at the problem of remaining useful life (RUL) prediction for lithium-ion batteries, an indirect prediction method based on expectile regression (ER) is proposed. First, several indirect health indicators which can characterize the performance decline of batteries are extracted from the charging process of batteries, and the correlations with capacity are analyzed using the Pearson and Spearman correlation analysis method. Second, a novel ER-extreme machine learning (ER-ELM) model is developed by combining with ELM. The BFGS quasi-Newton method is introduced to optimize the parameters of the ER-ELM model, and a lithium-ion battery RUL prediction model is established. Finally, the model is verified using the CALCE lithium-ion battery data set. Results show that compared with the standard ELM model, the ER-ELM model has stable output of prediction results, the maximum root mean square error of prediction results is 0.028 9, and all the values of goodness of fit are above 98%, indicating a better performance in terms of tracking capacity decline and regeneration.

Key words

Lithium-ion battery / remaining useful life (RUL) / expectile regression / extreme learning machine (ELM) / quasi-Newton method

引用本文

聂灶金, 何晓霞, 吴传菊. 基于ER-ELM模型的锂离子电池剩余使用寿命预测. 电源学报, 2026 , 24 (3) : 249 -258 . DOI: 10.13234/j.issn.2095-2805.2026.3.249

Zaojin NIE, Xiaoxia HE, Chuanju WU. Remaining Useful Life Prediction for Lithium-ion Batteries Based on ER-ELM Model[J]. Journal of Power Supply, 2026 , 24 (3) : 249 -258 . DOI: 10.13234/j.issn.2095-2805.2026.3.249

基金

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冶金工业过程系统科学湖北省重点实验室资助项目(Y202201)

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2026年第24卷第3期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.3.249

接收时间：2023-05-08
首发时间：2026-10-29
出版时间：2026-03-30

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出版历史

收稿日期：2023-05-08
修回日期：2023-08-24
录用日期：2023-11-21

基金

Hubei Key Laboratory for Systems Science in Metallurgical Processes(Y202201)

冶金工业过程系统科学湖北省重点实验室资助项目(Y202201)

作者信息

^1.武汉科技大学理学院，武汉　430065

^2.武汉科技大学冶金工业过程系统科学湖北省重点实验室，武汉　430081

通讯作者:

何晓霞（1979—　），女，通信作者，博士，教授。研究方向：高维数据的统计推断。E-mail：hexiaoxia@wust.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.3.249

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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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