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State-of-health Estimation of Lithium-ion Batteries Based on Health Factors and PSO-LSTM
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Xuefeng NING1, Zhenglu SHI  Member, CPSS2, Jiazhu XU2
Journal of Power Supply | 2026, 24(2) : 223 - 232
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Journal of Power Supply | 2026, 24(2): 223-232
• AI and Power Electronics •
State-of-health Estimation of Lithium-ion Batteries Based on Health Factors and PSO-LSTM
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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
Published: 2026-02-28 doi: 10.13234/j.issn.2095-2805.2026.2.223
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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).

Health factor  /  lithium-ion batteries (LIBs)  /  improved long short-term memory (LSTM) neural network  /  state-of-health (SOH) estimation
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
Funding
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  • Science and Technology Project of China Southern Power Grid Corporation(031900KK52220037)
  • National Natural Science Foundation of China(52077069)
Appendix
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Year 2026 volume 24 Issue 2
40
7
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.2.223
  • Receive Date：2023-03-23
  • Online Date：2026-10-29
  • Published：2026-02-28
Article Data
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History
  • Received：2023-03-23
  • Revised：2023-07-27
  • Accepted：2023-09-10
Funding
Science and Technology Project of China Southern Power Grid Corporation(031900KK52220037)
National Natural Science Foundation of China(52077069)
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
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https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2026.2.223
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表12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
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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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