电源学报

基于改进HTS-GRU的锂离子电池寿命预测

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林冬丽 , 张菁中国电源学会会员

电源学报 | 新能源发电与储能 2026,24(6): 189-197

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电源学报 |新能源发电与储能 2026 , 24 (6) : 189 -197

基于改进HTS-GRU的锂离子电池寿命预测

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林冬丽, 张菁中国电源学会会员

作者信息

上海工程技术大学电子电气工程学院，上海　201620

通讯作者:

张菁（1969—　），女，中国电源学会会员，通信作者，硕士，副教授。研究方向：电气控制应用，人工智能及锂电池寿命预测。E-mail：zj2015@sues.edu.cn。

作者简介:

林冬丽（1997—　），女，硕士研究生。研究方向：锂离子电池寿命预测。E-mail：m320122334@sues.edu.cn。

Lithium-ion Battery Life Prediction Based on IHTS-GRU

Dongli LIN, Jing ZHANG Member, CPSS

Affiliations

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

出版时间: 2026-06-30 doi: 10.13234/j.issn.2095-2805.2026.6.189

文章导航

摘要

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为了提高锂离子电池剩余使用寿命RUL（remaining useful life）预测的精度，提出一种结合完全自适应噪声集合经验模态分解CEEMDAN（complete ensemble empirical mode decomposition with adaptive noise）、随机森林RF（random forest）、改进榛子树搜索IHTS（improved hazelnut tree search）算法和改进门控循环单元GRU（gated recurrent unit）预测锂离子电池RUL的模型。首先利用CEEMDAN对原始容量数据进行模态分解降噪；其次利用RF计算各个模态分量相对于原始容量数据的权重；再次利用混沌初始化优化改进榛子树搜索算法得到IHTS算法，然后使用IHTS优化改进GRU得到IHTS-GRU模型；最后将各个模态分量IHTS-GRU预测的结果和各个模态分量权重加权平均，进而得到锂离子电池RUL预测结果。通过对美国国家航空航天局NASA的数据进行验证，CEEMDAN-RF-IHTS-GRU模型相对于CEEMDAN-GRU模型预测精度平均提高了3.2%，相对于CEEMDAN-RF-GRU模型预测精度平均提高了2.1%，相对于锂电池RUL常用模型预测精度明显提高，证明该方法提高了锂离子电池RUL预测的精度。

关键词

锂离子电池 / 剩余使用寿命 / 随机森林 / 榛子树搜索算法 / 门控循环单元

Abstract

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To improve the accuracy of remaining useful life (RUL) prediction for lithium-ion batteries, a method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), random forest (RF), improved hazelnut tree search (IHTS) algorithm and improved gated recurrent unit (GRU) is proposed. First, CEEMDAN is used for modal decomposition and noise reduction on the original capacity data. Second, RF is adopted to calculate the weight of each modal component relative to the original capacity data. Third, chaos initialization optimization is employed to improve a hazelnut tree search algorithm to obtain the IHTS algorithm, which is further used to optimize and improve GRU to establish an IHTS-GRU model. Finally, the IHTS-GRU prediction results of each modal component and the weight of each modal component are weighted and averaged, thus realizing the lithium-ion battery RUL prediction. The NASA data is used for verification, and it is found that the prediction accuracy of the CEEMDAN-RF-IHTS-GRU model is improved by 3.2% and 2.1% on average compared with those of the CEEMDAN-GRU and CEEMDAN-RF-GRU models, respectively, indicating significant increases in the prediction accuracy. Therefore, the proposed method improves the accuracy of RUL prediction for lithium-ion batteries.

Key words

Lithium-ion battery / remaining useful life (RUL) / random forest (RF) / hazelnut tree search (HTS) algorithm / gated recurrent unit (GRU)

引用本文

林冬丽, 张菁中国电源学会会员. 基于改进HTS-GRU的锂离子电池寿命预测. 电源学报, 2026 , 24 (6) : 189 -197 . DOI: 10.13234/j.issn.2095-2805.2026.6.189

Dongli LIN, Jing ZHANG. Lithium-ion Battery Life Prediction Based on IHTS-GRU[J]. Journal of Power Supply, 2026 , 24 (6) : 189 -197 . DOI: 10.13234/j.issn.2095-2805.2026.6.189

基金

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国家自然科学基金资助项目(51707117)

参考文献

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2026年第24卷第6期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.6.189

接收时间：2024-01-18
首发时间：2026-10-29
出版时间：2026-06-30

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出版历史

收稿日期：2024-01-18
修回日期：2024-04-07
录用日期：2024-07-02

基金

National Natural Science Foundation of China(51707117)

国家自然科学基金资助项目(51707117)

作者信息

上海工程技术大学电子电气工程学院，上海　201620

通讯作者:

张菁（1969—　），女，中国电源学会会员，通信作者，硕士，副教授。研究方向：电气控制应用，人工智能及锂电池寿命预测。E-mail：zj2015@sues.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.6.189

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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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