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Joint SOC-SOH Estimation of Lithium-ion Batteries Based on Three-stage Parameter Identification
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Jianmin HOU1, 2, Haifeng QIAN1, Zixiang LI1, Wenjing YANG1
Journal of Power Supply | 2026, 24(9) : 132 - 144
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Journal of Power Supply | 2026, 24(9): 132-144
• Renewable Energy Generation and Energy Storage •
Joint SOC-SOH Estimation of Lithium-ion Batteries Based on Three-stage Parameter Identification
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Jianmin HOU1, 2, Haifeng QIAN1, Zixiang LI1, Wenjing YANG1
Affiliations
  • 1.College of Automation Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 2.Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Changzhou 213022, China
Published: 2026-09-30 doi: 10.13234/j.issn.2095-2805.2026.9.132
Outline
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The precise identification of model parameters for lithium-ion batteries is crucial for the observing of remaining battery capacity and extending the service life. However, traditional parameter identification methods are often affected by parameter coupling, getting trapped in a local optimum, and other factors, which reduce the estimation accuracy and subsequently lower the accuracy of the subsequent joint estimation. Therefore, an SOC-SOH joint estimation based on a three-stage parameter identification method is proposed. In the first stage, an RC model with fast and slow dynamic modules is constructed based on the internal dynamic characteristics of the battery. In the second stage, the fast dynamic (FD) module uses the forgetting factor recursiveleast squares (FFRLS) adaptive parameter identification method to identify the parameters R0, R1, and C1 with a forgetting factor. In the third stage, the slow dynamic (SD) module uses the adaptive extended Kalman filter algorithm (AEKF) for online parameter identification, and combines the zebra optimization algorithm (ZOA) to solve the local optimum problem and further improve the accuracy of the parameter identification of UOC, R2, and C2. Considering that battery capacity and battery state have different time-varying characteristics, a multi-time scale method is used for the SOC-SOH joint estimation to improve the estimation accuracy. The simulation results show that the improved joint estimation method has better estimation characteristics than traditional methods in terms of average error and maximum error of SOC estimation under different temperatures and operating conditions.

Zebra optimization algorithm (ZOA)  /  dual adaptive extended Kalman filter (DAEKF) algorithm  /  state of charge (SOC)  /  state of health (SOH)  /  joint estimation
侯健敏, 钱海峰, 李紫祥, 杨文静. 基于三阶段参数辨识的锂离子电池SOC-SOH联合估计. 电源学报, 2026 , 24 (9) : 132 -144 . DOI: 10.13234/j.issn.2095-2805.2026.9.132
Jianmin HOU, Haifeng QIAN, Zixiang LI, Wenjing YANG. Joint SOC-SOH Estimation of Lithium-ion Batteries Based on Three-stage Parameter Identification[J]. Journal of Power Supply, 2026 , 24 (9) : 132 -144 . DOI: 10.13234/j.issn.2095-2805.2026.9.132
  • National Natural Science Foundation of China(52077105)
  • Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology(2023JSSPD12)
Year 2026 volume 24 Issue 9
33
5
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.9.132
  • Receive Date:2025-01-04
  • Online Date:2026-10-29
  • Published:2026-09-30
Article Data
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History
  • Received:2025-01-04
  • Revised:2025-02-19
  • Accepted:2025-02-28
Funding
National Natural Science Foundation of China(52077105)
Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology(2023JSSPD12)
Affiliations
    1.College of Automation Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
    2.Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Changzhou 213022, 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
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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