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State-of-health Estimation Method for Lithium-ion Battery Considering Capacity Regeneration
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Jingying ZHAO1, Linran HUANG1, Shuailiang YAO2
Journal of Power Supply | 2026, 24(1) : 242 - 251
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Journal of Power Supply | 2026, 24(1): 242-251
• Renewable Energy Generation and Energy Storage •
State-of-health Estimation Method for Lithium-ion Battery Considering Capacity Regeneration
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Jingying ZHAO1, Linran HUANG1, Shuailiang YAO2
Affiliations
  • 1.State Key Laboratory of Reliability and Intelligence of Electrical Equipment, College of Electrical Engineering, Hebei University of Technology, Tianjin 300130, China
  • 2.State Grid Jibei Zhangjiakou Wind and Solar Energy Storage and Transportation New Energy Co., Ltd., Zhangjiakou 075000, China
Published: 2026-01-30 doi: 10.13234/j.issn.2095-2805.2026.1.242
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During the normal utilization of a lithium-ion battery, its capacity will be regenerated in a short term due to factors such as internal chemical reactions and external environment. Aimed at the problems including the inaccurate estimation of state-of-health (SOH) which results from the ignorance of capacity regeneration, an optimal variational mode decomposition (OVMD) strategy based on the number of optimal layers is proposed. The low-frequency stable capacity sequence reflects the overall aging trend, while the high-frequency non-smooth capacity sequence reflects the local capacity regeneration. With the consideration of high- and low-frequency components and by introducing the weighted mean of vectors (INFO) algorithm to improve the support vector regression (SVR), a short-term SOH estimation model for lithium-ion battery is established. With the selection of NASA battery aging data sets, contrast schemes for short-term lithium-ion battery SOH estimation experiment are designed based on BP (back propagation), SVR, INFO-SVR and OVMD-INFO-SVR. Results indicate that in the case of reducing the capacity training sets, the OVMD-INFO-SVR model based on high- and low-frequency components has higher accuracy when it is used in short-term SOH estimation of lithium-ion batteries.

Lithium-ion battery  /  capacity regeneration  /  support vector regression (SVR)  /  state-of-health (SOH)
赵靖英, 黄麟然, 姚帅亮. 考虑容量再生的锂离子电池健康状态估计方法. 电源学报, 2026 , 24 (1) : 242 -251 . DOI: 10.13234/j.issn.2095-2805.2026.1.242
Jingying ZHAO, Linran HUANG, Shuailiang YAO. State-of-health Estimation Method for Lithium-ion Battery Considering Capacity Regeneration[J]. Journal of Power Supply, 2026 , 24 (1) : 242 -251 . DOI: 10.13234/j.issn.2095-2805.2026.1.242
Funding
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  • National Natural Science Foundation of China(5137704)
  • Natural Science Foundation of Hebei Province(E2019202481; E2017202284)
Appendix
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Year 2026 volume 24 Issue 1
45
3
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.1.242
  • Receive Date：2023-05-29
  • Online Date：2026-10-26
  • Published：2026-01-30
Article Data
Affiliations
History
  • Received：2023-05-29
  • Revised：2023-08-28
  • Accepted：2023-09-22
Funding
National Natural Science Foundation of China(5137704)
Natural Science Foundation of Hebei Province(E2019202481; E2017202284)
Affiliations
    1.State Key Laboratory of Reliability and Intelligence of Electrical Equipment, College of Electrical Engineering, Hebei University of Technology, Tianjin 300130, China
    2.State Grid Jibei Zhangjiakou Wind and Solar Energy Storage and Transportation New Energy Co., Ltd., Zhangjiakou 075000, China
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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
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Percentage of
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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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