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State of Health Estimation Method for Lithium-ion Batteries in Energy Storage Systems Based on Two-stage Charging Data
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Yuanliang FAN1, 2, Junwei ZHU3, Han WU1, 2, Xiaolan HAN4, Xinghua HUANG1, 2, Jinyu CHEN1, 2
Electric Drive | 2025, 55(3) : 72 - 80
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Electric Drive | 2025, 55(3): 72-80
State of Health Estimation Method for Lithium-ion Batteries in Energy Storage Systems Based on Two-stage Charging Data
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Yuanliang FAN1, 2, Junwei ZHU3, Han WU1, 2, Xiaolan HAN4, Xinghua HUANG1, 2, Jinyu CHEN1, 2
Affiliations
  • 1 State Grid Fujian Electric Power Research Institute，Fuzhou 350007，Fujian，China
  • 2 Fujian Provincial Enterprise Key Laboratory of High Reliable Electric Power Distribution Technology，Fuzhou 350007，Fujian，China
  • 3 State Grid Fujian Electric Power Co.，Ltd. Putian Power Supply Company，Putian 351199，Fujian，China
  • 4 School of Automation，Guangdong University of Technology，Guangzhou 510006，Guangdong，China
Published: 2025-03-20 doi: 10.19457/j.1001-2095.dqcd25516
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Accurately estimating the state of health （SOH）of lithium-ion batteries is crucial for optimizing energy storage systems' operation，management，and maintenance. Existing methods that extract health features from single-stage charging data fail to exploit battery aging information fully，leading to a suboptimal estimation accuracy. In addressing this issue，a SOH estimation method for energy storage systems based on the fusion of two-stage charging data for lithium-ion batteries was proposed. Combining health features from both constant voltage charging and relaxation stages，the proposed method effectively mines aging information embedded in two-stage charging data，thereby improving SOH estimation accuracy. Additionally，the introduced health feature combination does not require the use of constant current charging stage data，making it less affected by the uncertainty of charging start points and more adaptable to practical energy storage conditions. Experimental results demonstrate that the proposed health feature combination significantly outperforms single-stage feature combinations，with an average absolute error of 0.66%，mean squared error of 0.85%，and an average coefficient of determination of 0.97.

lithium-ion batteries  /  state of health （SOH） estimation  /  two-stage features fusion
Yuanliang FAN, Junwei ZHU, Han WU, Xiaolan HAN, Xinghua HUANG, Jinyu CHEN. State of Health Estimation Method for Lithium-ion Batteries in Energy Storage Systems Based on Two-stage Charging Data[J]. Electric Drive, 2025 , 55 (3) : 72 -80 . DOI: 10.19457/j.1001-2095.dqcd25516
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Article Info
doi: 10.19457/j.1001-2095.dqcd25516
  • Receive Date：2023-11-28
  • Online Date：2025-11-26
  • Published：2025-03-20
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History
  • Received：2023-11-28
  • Revised：2023-12-09
Funding
Affiliations
    1 State Grid Fujian Electric Power Research Institute，Fuzhou 350007，Fujian，China
    2 Fujian Provincial Enterprise Key Laboratory of High Reliable Electric Power Distribution Technology，Fuzhou 350007，Fujian，China
    3 State Grid Fujian Electric Power Co.，Ltd. Putian Power Supply Company，Putian 351199，Fujian，China
    4 School of Automation，Guangdong University of Technology，Guangzhou 510006，Guangdong，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
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Genus		 种数
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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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