电源学报

基于健康因子和PSO-LSTM的锂离子电池健康状态估计

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宁雪峰 ¹ , 石正禄中国电源学会会员 ² , 许加柱 ²

电源学报 | 人工智能与电力电子 2026,24(2): 223-232

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电源学报 |人工智能与电力电子 2026 , 24 (2) : 223 -232

基于健康因子和PSO-LSTM的锂离子电池健康状态估计

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宁雪峰¹, 石正禄中国电源学会会员², 许加柱²

作者信息

^1.广东电网有限责任公司东莞供电局，东莞　523000

^2.湖南大学电气与信息工程学院，长沙　410082

通讯作者:

许加柱（1980—　），男，通信作者，博士，教授。研究方向：交直流电能变换新技术。E-mail：xjz@hnu.edu.cn。

作者简介:

宁雪峰（1980—　），男，硕士，高级工程师。研究方向：大规模储能技术，新能源与分布式发电。E-mail：754082537@qq.com。

石正禄（1996—　），男，中国电源学会会员，博士研究生。研究方向：大规模储能技术。E-mail：zlshi0305@hnu.edu.cn。

State-of-health Estimation of Lithium-ion Batteries Based on Health Factors and PSO-LSTM

Xuefeng NING¹, Zhenglu SHI Member, CPSS², Jiazhu XU²

Affiliations

^1.Dongguan Power Supply Bureau, Guangdong Power Grid Co., Ltd., Dongguan 523000, China

^2.College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

出版时间: 2026-02-28 doi: 10.13234/j.issn.2095-2805.2026.2.223

文章导航

摘要

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锂离子电池LIBs（lithium-ion batteries）健康状态SOH（state-of-health）是表征电池实际寿命的关键参数。为了提高锂离子电池SOH估计精度，提出了1种基于健康因子和改进的长短期记忆LSTM（long short-term memory）神经网络的SOH估计方法。首先选取10个与SOH变化高度相关的健康因子作为SOH特征，然后引入粒子群优化PSO（particle swarm optimization）算法对LSTM超参数进行寻优，构建PSO-LSTM估计模型，最后用提取的健康因子作为模型输入实现SOH的准确估计。结果表明，分别以整个样本70%、60%和50%的数据作为训练集，剩余数据作为测试集，其SOH估计误差主要分布在2%以内。同时与现有的LSTM和前馈神经网络FNN（feed-forward neural networks）相比，PSO-LSTM表现出较高的估计精度和较好的鲁棒性。

关键词

健康因子 / 锂离子电池 / 改进的长短期记忆神经网络 / 健康状态估计

Abstract

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The state-of-health (SOH) of lithium-ion batteries (LIBs) is a key parameter that characterizes their real lifespan. To improve the accuracy of SOH estimation for LIBs, a novel SOH estimation method based on health factors and improved long short-term memory (LSTM) neural network is proposed. First, ten health factors that are highly correlated with the changes in SOH are selected as the characteristics of SOH. Second, the particle swarm optimization (PSO) algorithm is introduced to optimize the hyper-parameter of the LSTM neural network, and an estimation model based on PSO-LSTM is constructed. Finally, the extracted health factors are used as model input to achieve an accurate estimation of SOH. Results show that when 70%, 60% and 50% of data from the whole sample are taken as the training set respectively and the remaining data is taken as the test set, the SOH estimation errors are mainly distributed in a range of less than 2%. Meanwhile, the PSO-LSTM model demonstrates higher estimation accuracy and better robustness compared with the existing LSTM and feed-forward neural networks (FNN).

Key words

Health factor / lithium-ion batteries (LIBs) / improved long short-term memory (LSTM) neural network / state-of-health (SOH) estimation

引用本文

宁雪峰, 石正禄中国电源学会会员, 许加柱. 基于健康因子和PSO-LSTM的锂离子电池健康状态估计. 电源学报, 2026 , 24 (2) : 223 -232 . DOI: 10.13234/j.issn.2095-2805.2026.2.223

Xuefeng NING, Zhenglu SHI, Jiazhu XU. State-of-health Estimation of Lithium-ion Batteries Based on Health Factors and PSO-LSTM[J]. Journal of Power Supply, 2026 , 24 (2) : 223 -232 . DOI: 10.13234/j.issn.2095-2805.2026.2.223

基金

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南方电网公司科技资助项目(031900KK52220037)
国家自然科学基金资助项目(52077069)

参考文献

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2026年第24卷第2期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.2.223

接收时间：2023-03-23
首发时间：2026-10-29
出版时间：2026-02-28

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出版历史

收稿日期：2023-03-23
修回日期：2023-07-27
录用日期：2023-09-10

基金

Science and Technology Project of China Southern Power Grid Corporation(031900KK52220037)

南方电网公司科技资助项目(031900KK52220037)

National Natural Science Foundation of China(52077069)

国家自然科学基金资助项目(52077069)

作者信息

^1.广东电网有限责任公司东莞供电局，东莞　523000

^2.湖南大学电气与信息工程学院，长沙　410082

通讯作者:

许加柱（1980—　），男，通信作者，博士，教授。研究方向：交直流电能变换新技术。E-mail：xjz@hnu.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.2.223

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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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