电源学报

基于EMD-LSTM-Attention的锂离子电池SOC预测

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朱博 , 刘雪芹 , 王宇珲 , 李涛 , 程观银

电源学报 | 人工智能与电力电子 2026,24(3): 242-248

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电源学报 |人工智能与电力电子 2026 , 24 (3) : 242 -248

基于EMD-LSTM-Attention的锂离子电池SOC预测

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朱博（1997—　），男，硕士研究生。研究方向：锂离子电池荷电状态预测。E-mail：347947453@qq.com。

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朱博（1997—　），男，硕士研究生。研究方向：锂离子电池荷电状态预测。E-mail：347947453@qq.com。

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王宇珲（1996—　），男，硕士。研究方向：时间序列预测。E-mail：756794766@qq.com。

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王宇珲（1996—　），男，硕士。研究方向：时间序列预测。E-mail：756794766@qq.com。

李涛（1996—　），男，硕士。研究方向：时间序列预测。E-mail：15523043058@163.com。

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李涛（1996—　），男，硕士。研究方向：时间序列预测。E-mail：15523043058@163.com。

程观银（1997—　），男，硕士。研究方向：新能源材料与器件。E-mail：1804807967@qq.com。

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程观银（1997—　），男，硕士。研究方向：新能源材料与器件。E-mail：1804807967@qq.com。

朱博, 刘雪芹, 王宇珲, 李涛, 程观银

作者信息

重庆理工大学理学院，重庆　400054

通讯作者:

刘雪芹（1971—　），男，通信作者，博士，教授。研究方向：新能源材料与器件，人工智能。E-mail：xqliu@cqut.edu.cn。

作者简介:

朱博（1997—　），男，硕士研究生。研究方向：锂离子电池荷电状态预测。E-mail：347947453@qq.com。

王宇珲（1996—　），男，硕士。研究方向：时间序列预测。E-mail：756794766@qq.com。

李涛（1996—　），男，硕士。研究方向：时间序列预测。E-mail：15523043058@163.com。

程观银（1997—　），男，硕士。研究方向：新能源材料与器件。E-mail：1804807967@qq.com。

SOC Prediction for Lithium-ion Batteries Based on EMD-LSTM-Attention

Bo ZHU, Xueqin LIU, Yuhui WANG, Tao LI, Guanyin CHENG

Affiliations

School of Science, Chongqing University of Technology, Chongqing 400054, China

出版时间: 2026-03-30 doi: 10.13234/j.issn.2095-2805.2026.3.242

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摘要

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锂离子电池荷电状态SOC（state-of-charge）预测是电池管理系统中重要的一环，精确地预测锂离子电池SOC对保障电池安全运行具有重要意义。针对锂离子电池SOC的预测问题，利用经验模态分解EMD（empirical mode decomposition）、长短期记忆LSTM（long short-term memory）循环神经网络与注意力（Attention）机制搭建锂离子电池SOC预测模型。以锂离子电池充放电过程中的电压、电流、表面温度与经过EMD分解后的电压作为模型的特征；模型的LSTM层能够从锂离子电池历史循环数据中捕捉电池时间信息，并总结出长期的依赖关系；模型的注意力层能够捕捉特征输入和SOC输出之间的非线性和动态关系，优化各循环阶段和各个特征之间的权重关系，进一步提高SOC预测精度。使用马里兰大学公共电池数据集进行实验，结果表明与仅使用LSTM的模型相比，所提模型在3种不同工况下的平均预测误差分别降低了49.36%、45.61%、46.93%。

关键词

锂离子电池 / 荷电状态 / 经验模态分解 / 长短期记忆神经网络 / 注意力机制

Abstract

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The state-of-charge (SOC) prediction for lithium-ion batteries is an important part of a battery management system, and accurately predicting their SOC is of significance for ensuring their safe operation. On this basis, empirical mode decomposition (EMD), long short-term memory (LSTM) recurrent neural network and Attention mechanism are used to build an SOC prediction model for a lithium-ion battery. The voltage, current, surface temperature and the voltage decomposed by EMD in its charging and discharging processes are taken as the characteristics of the model. The LSTM layer of the model can capture the battery time information from the historical cycle data of the lithium-ion battery, and the long-term dependence relationship is also summarized. The Attention layer of the model can capture the nonlinear and dynamic relationship between feature input and SOC output, so as to optimize the weight relationship between each cycle stage and each feature and further improve the SOC prediction accuracy. Experiments were carried out based on the public battery data set of University of Maryland, and results show that compared with that of a model which only uses LSTM, the average prediction errors of the proposed model under three different working conditions were reduced by 49.36%, 45.61% and 46.93%, respectively.

Key words

Lithium-ion battery / state-of-charge (SOC) / empirical mode decomposition (EMD) / long short-term memory (LSTM) neural network / Attention mechanism

引用本文

朱博, 刘雪芹, 王宇珲, 李涛, 程观银. 基于EMD-LSTM-Attention的锂离子电池SOC预测. 电源学报, 2026 , 24 (3) : 242 -248 . DOI: 10.13234/j.issn.2095-2805.2026.3.242

Bo ZHU, Xueqin LIU, Yuhui WANG, Tao LI, Guanyin CHENG. SOC Prediction for Lithium-ion Batteries Based on EMD-LSTM-Attention[J]. Journal of Power Supply, 2026 , 24 (3) : 242 -248 . DOI: 10.13234/j.issn.2095-2805.2026.3.242

基金

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国家自然科学基金资助项目(11774041)
重庆市基础与前沿研究计划资助项目(cstc2015jcyjA50033)
重庆理工大学研究生教育高质量发展资助项目(gzlcx20222079)

参考文献

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参考文献引证文献

排序方式：

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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.2095-2805.2026.3.242

接收时间：2023-02-02
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：2023-02-02
修回日期：2023-09-14
录用日期：2023-12-13

基金

National Natural Science Foundation of China(11774041)

国家自然科学基金资助项目(11774041)

Chongqing Research Program of Basic Research and Frontier Technology(cstc2015jcyjA50033)

重庆市基础与前沿研究计划资助项目(cstc2015jcyjA50033)

Chongqing University of Technology Graduate Education High Quality Development Program(gzlcx20222079)

重庆理工大学研究生教育高质量发展资助项目(gzlcx20222079)

作者信息

重庆理工大学理学院，重庆　400054

通讯作者:

刘雪芹（1971—　），男，通信作者，博士，教授。研究方向：新能源材料与器件，人工智能。E-mail：xqliu@cqut.edu.cn。

参考文献

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.3.242

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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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