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SOH Estimation of Lithium-ion Batteries Based on Multiple Feature Combinations
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Han WU1, 2, Xinghua HUANG1, 2, Zhendong QIAO3, Yuanliang FAN1, 2, Junwei ZHU4, Jinyu CHEN1, 2
Electric Drive | 2025, 55(1) : 25 - 32
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Electric Drive | 2025, 55(1): 25-32
SOH Estimation of Lithium-ion Batteries Based on Multiple Feature Combinations
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Han WU1, 2, Xinghua HUANG1, 2, Zhendong QIAO3, Yuanliang FAN1, 2, Junwei ZHU4, Jinyu CHEN1, 2
Affiliations
  • 1 Electric Power Research Institute of State Grid Fujian Electric Power Co.，Ltd.，Fuzhou 350007，Fujian，China
  • 2 Fujian Provincial Enterprise Key Laboratory of High Reliable Electric Power Distribution Technology，Fuzhou 350007，Fujian，China
  • 3 School of Automation，Guangdong University of Technology，Guangzhou 510006，Guangdong，China
  • 4 State Grid Fujian Electric Power Co.，Ltd. Putian Power Supply Company，Putian 351199，Fujian，China
Published: 2025-01-20 doi: 10.19457/j.1001-2095.dqcd25435
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Accurately estimating the state of health（SOH）of lithium-ion batteries is a crucial prerequisite for ensuring the safe and stable operation of energy storage systems. The key to improving the accuracy of SOH estimation lies in the rational selection of health characteristics that can effectively reflect the state of health of lithium-ion batteries. By analyzing the current characteristics of lithium-ion batteries during the constant voltage charging stage，a healthy combination of features containing the slope of the first and last points of the current curve，the standard deviation，and the mean value were extracted from the current curve data during the constant voltage charging stage. To validate the effectiveness of the proposed feature combination，SOH estimation model based on kernel ridge regression（KRR）and support vector regression（SVR）was designed，and model validation was successfully completed. The experimental results demonstrate that the proposed feature combination can achieve high-precision SOH estimation across different models，exhibiting excellent model adaptability.

lithium-ion battery  /  state of health（SOH） estimation  /  constant voltage charging stage  /  kernel ridge regression（KRR）  /  support vector regression（SVR）
Han WU, Xinghua HUANG, Zhendong QIAO, Yuanliang FAN, Junwei ZHU, Jinyu CHEN. SOH Estimation of Lithium-ion Batteries Based on Multiple Feature Combinations[J]. Electric Drive, 2025 , 55 (1) : 25 -32 . DOI: 10.19457/j.1001-2095.dqcd25435
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Article Info
doi: 10.19457/j.1001-2095.dqcd25435
  • Receive Date：2023-10-18
  • Online Date：2025-10-29
  • Published：2025-01-20
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History
  • Received：2023-10-18
  • Revised：2023-12-21
Funding
Affiliations
    1 Electric Power Research Institute of State Grid Fujian Electric Power Co.，Ltd.，Fuzhou 350007，Fujian，China
    2 Fujian Provincial Enterprise Key Laboratory of High Reliable Electric Power Distribution Technology，Fuzhou 350007，Fujian，China
    3 School of Automation，Guangdong University of Technology，Guangzhou 510006，Guangdong，China
    4 State Grid Fujian Electric Power Co.，Ltd. Putian Power Supply Company，Putian 351199，Fujian，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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