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Estimation of Lithium-ion Battery State of Health Based on Stacking Model
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Ertao LEI1, Junkun ZHANG1, Jinyu WANG2, Li JIN1, Kai MA1, Ying LI1
Journal of Power Supply | 2026, 24(5) : 248 - 255
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Journal of Power Supply | 2026, 24(5): 248-255
• AI and Power Electronics •
Estimation of Lithium-ion Battery State of Health Based on Stacking Model
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Ertao LEI1, Junkun ZHANG1, Jinyu WANG2, Li JIN1, Kai MA1, Ying LI1
Affiliations
  • 1.Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
  • 2.School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Published: 2026-05-30 doi: 10.13234/j.issn.2095-2805.2026.5.248
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Accurate estimation of the battery state of health (SOH) can effectively improve the safe operation of system. Aiming at the problem of unstable SOH estimation accuracy under different working conditions, a stacking model-based method for lithium-ion battery SOH estimation is proposed. The method firstly carries out incremental capacity (IC) analysis for the charging data, and extracts the health factor as an effective feature, and then constructs an stacking model by stacking the support vector regression (SVR) and back propagation (BP) neural network model to realize the on-line evaluation of the battery SOH. The experimental results show that the maximum error of the stacking model proposed decreases by 38.1% and 23.7%, and the root mean square error (RMSE) decreases by 47% and 20% compared with the two sub-models. The proposed method can more accurately estimate the SOH under various operating conditions, mitigating the instability factor of the single model estimation.

Lithium-ion battery  /  incremental capacity (IC) analysis  /  estimation of state of health (SOH)  /  stacking model
雷二涛, 张浚坤, 王瑾瑜, 金莉, 马凯, 李盈. 基于集成模型的锂离子电池SOH估计. 电源学报, 2026 , 24 (5) : 248 -255 . DOI: 10.13234/j.issn.2095-2805.2026.5.248
Ertao LEI, Junkun ZHANG, Jinyu WANG, Li JIN, Kai MA, Ying LI. Estimation of Lithium-ion Battery State of Health Based on Stacking Model[J]. Journal of Power Supply, 2026 , 24 (5) : 248 -255 . DOI: 10.13234/j.issn.2095-2805.2026.5.248
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Year 2026 volume 24 Issue 5
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.5.248
  • Receive Date：2024-02-28
  • Online Date：2026-10-29
  • Published：2026-05-30
Article Data
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History
  • Received：2024-02-28
  • Revised：2024-04-09
  • Accepted：2024-04-17
Affiliations
    1.Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China
    2.School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
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https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2026.5.248
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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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