电源学报

基于源域电池迭代模块和LSTM神经网络的锂离子电池寿命预测

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王子鹏中国电源学会学生会员 ¹^,² , 黄凯中国电源学会会员 ¹^,² , 孙恺 ¹^,² , 李森茂 ¹^,²

电源学报 | 电池与储能 2025,23(7): 294-303

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电源学报 |电池与储能 2025 , 23 (7) : 294 -303

基于源域电池迭代模块和LSTM神经网络的锂离子电池寿命预测

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王子鹏中国电源学会学生会员¹^,², 黄凯中国电源学会会员¹^,², 孙恺¹^,², 李森茂¹^,²

作者信息

^1.省部共建电工装备可靠性与智能化国家重点实验室（河北工业大学），天津　300130

^2.河北省电磁场与电器可靠性重点实验室（河北工业大学），天津　300130

通讯作者:

黄凯（1980—　），男，中国电源学会会员，通信作者，博士，副教授。研究方向：电器可靠性理论及检测技术，储能电池组健康状态预测与可靠性评估。E-mail：huangkai@hebut.edu.cn。

作者简介:

王子鹏（1997—　），男，中国电源学会学生会员，硕士研究生。研究方向：电器可靠性理论及检测技术，储能电池组建模，寿命预测。E-mail：1640248762@qq.com。

孙恺（1997—　），男，硕士研究生。研究方向：电器可靠性理论及检测技术，储能电池组建模，寿命预测。E-mail：202031404021@stu.hebut.edu.cn。

李森茂（1998—　），男，硕士研究生。研究方向：电器可靠性理论及检测技术，储能电池组建模，寿命预测。E-mail：202031404026@stu.hebut.edu.cn。

Prediction of Remaining Useful Life of Lithium-ion Battery Based on Source-domain Battery Iterations Module and LSTM Neural Network

Zipeng WANG Student Member, CPSS¹^,², Kai HUANG Member, CPSS¹^,², Kai SUN¹^,², Senmao Li¹^,²

Affiliations

^1.State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China

^2.Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China

出版时间: 2025-11-30 doi: 10.13234/j.issn.2095-2805.2025.7.294

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摘要

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锂离子电池的剩余可使用寿命RUL（remaining useful life）预测是电池健康管理的重要组成部分。为提高RUL的预测精度，提出1种基于源域电池迭代模块SIM（source domain battery iterations module）与长短期记忆LSTM（long short-term memory）神经网络相融合的电池RUL预测方法。首先，将多块已知退化趋势的电池作为源域电池，并使用其全寿命容量退化数据构建SIM以获得最优LSTM预训练模型。其次，将预训练模型迁移至目标域中并使用目标域训练集对模型微调。最后，将微调后的LSTM预训练模型用于目标域电池的容量预测任务并完成RUL预测。采用3种开源数据集验证算法的有效性。结果表明，该方法在源域电池和目标域电池类型相同的情况下RUL预测值绝对误差小于2圈；电池类型不同的情况下，除20%预测起点外RUL预测误差均小于10圈，具有较高的预测精度。

关键词

锂离子电池 / 剩余使用寿命 / 迁移学习 / 长短期记忆神经网络 / 源域电池迭代模块

Abstract

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The remaining useful life (RUL) prediction of lithium-ion batteries is an important part of battery health management. To improve the accuracy of RUL prediction, a battery RUL prediction method based on the fusion of source-domain battery iterations module (SIM) and long short-term memory (LSTM) neural network is proposed. First, multiple batteries with known degradation trends are used as source-domain batteries, and their full-life capacity degradation data is used to construct an SIM to obtain the optimal LSTM pre-training model. Second, the pre-training model is transferred to the target-domain and further fine-tuned using the target-domain training set. Finally, the fine-tuned LSTM pre-training model is used for the capacity prediction task of the target-domain battery and completes the RUL prediction. Three open-source datasets were used to validate the effectiveness of the algorithm. Results show that the RUL prediction error of this algorithm is less than 2 cycles for the same type of source-domain and target-domain batteries. For different battery types, the RUL prediction error is less than 10 cycles except for 20% of the starting points for prediction, indicating a high level of prediction accuracy.

Key words

Lithium-ion battery / remaining useful life (RUL) / transfer learning / long short-term memory (LSTM) neural network / source-domain battery iterations module (SIM)

引用本文

王子鹏中国电源学会学生会员, 黄凯中国电源学会会员, 孙恺, 李森茂. 基于源域电池迭代模块和LSTM神经网络的锂离子电池寿命预测. 电源学报, 2025 , 23 (7) : 294 -303 . DOI: 10.13234/j.issn.2095-2805.2025.7.294

Zipeng WANG, Kai HUANG, Kai SUN, Senmao Li. Prediction of Remaining Useful Life of Lithium-ion Battery Based on Source-domain Battery Iterations Module and LSTM Neural Network[J]. Journal of Power Supply, 2025 , 23 (7) : 294 -303 . DOI: 10.13234/j.issn.2095-2805.2025.7.294

基金

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河北省自然科学基金资助项目(E2019202328)

参考文献

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参考文献引证文献

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2025年第23卷第7期

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文章信息

doi: 10.13234/j.issn.2095-2805.2025.7.294

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

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出版历史

收稿日期：2022-10-18
修回日期：2022-12-29
录用日期：2023-02-09

基金

Natural Foundation of Hebei Province(E2019202328)

河北省自然科学基金资助项目(E2019202328)

作者信息

^1.省部共建电工装备可靠性与智能化国家重点实验室（河北工业大学），天津　300130

^2.河北省电磁场与电器可靠性重点实验室（河北工业大学），天津　300130

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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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