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Online Estimation of SOH for Lithium-ion Batteries Based on Incremental Capacity Curves and QGA-BP
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Yongmao ZHU  Student Member, CPSS1, Yin LÜ2, Jianwei SHAO  Senior Member, CPSS1, Mengyuan WANG  Student Member, CPSS1, Huiying ZHANG  Student Member, CPSS1, Junhua WANG1
Journal of Power Supply | 2026, 24(9) : 121 - 131
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Journal of Power Supply | 2026, 24(9): 121-131
• Renewable Energy Generation and Energy Storage •
Online Estimation of SOH for Lithium-ion Batteries Based on Incremental Capacity Curves and QGA-BP
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Yongmao ZHU  Student Member, CPSS1, Yin LÜ2, Jianwei SHAO  Senior Member, CPSS1, Mengyuan WANG  Student Member, CPSS1, Huiying ZHANG  Student Member, CPSS1, Junhua WANG1
Affiliations
  • 1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
  • 2.State Grid Hubei Electric Power Co., Ltd., Huangshi Power Supply Company, Huangshi 435000, China
Published: 2026-09-30 doi: 10.13234/j.issn.2095-2805.2026.9.121
Outline
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Accurate estimation of the state of health (SOH) of lithium-ion batteries can effectively ensure the safe operation of battery systems. To address the difficulties in selecting health indicators (HIs) and the problem that computational methods are prone to falling into local optimal solutions in the process of estimating the SOH of lithium-ion batteries, a lithium-ion battery SOH online estimation model based on incremental capcity (IC) of quantum genetic algorithm optimized back-propagation (QGA-BP) neural network is proposed. This algorithm first extracts key indicators from the IC curves during battery aging, subsequently conducts a correlation analysis between the extracted HIs and the SOH, then uses the extracted indicators as input to train the QGA-BP neural network to achieve online estimation of the SOH of lithium-ion batteries, and finally verified by the experimental data measured from the experimental platform and the NASA dataset. The results show that the maximum values of the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the proposed algorithm are 0.013 12, 0.012 94 and 0.017 86, respectively, compared with the traditional back-propagation (BP) neural network and other algorithm, with good convergence and robustness, and can achieve the accurate online estimation of SOH of lithium-ion batteries.

Lithium-ion batteries  /  state of health (SOH)  /  incremental capacity (IC)  /  quantum genetic algorithm optimized back-propagation (QGA-BP) neural network
朱永茂中国电源学会学生会员, 吕印, 邵建伟中国电源学会高级会员, 王梦园中国电源学会学生会员, 张慧颖中国电源学会学生会员, 王军华. 基于容量增量曲线的QGA-BP锂离子电池SOH在线估计. 电源学报, 2026 , 24 (9) : 121 -131 . DOI: 10.13234/j.issn.2095-2805.2026.9.121
Yongmao ZHU, Yin LÜ, Jianwei SHAO, Mengyuan WANG, Huiying ZHANG, Junhua WANG. Online Estimation of SOH for Lithium-ion Batteries Based on Incremental Capacity Curves and QGA-BP[J]. Journal of Power Supply, 2026 , 24 (9) : 121 -131 . DOI: 10.13234/j.issn.2095-2805.2026.9.121
Funding
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  • State Key Research and Development Program(2017YFB1201003)
  • China Postdoctoral Science Foundation(2024M752470)
  • Qinghai Provincial Science and Technology Project(2024-HN-P01)
Appendix
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Year 2026 volume 24 Issue 9
50
11
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.9.121
  • Receive Date：2024-09-11
  • Online Date：2026-10-29
  • Published：2026-09-30
Article Data
Affiliations
History
  • Received：2024-09-11
  • Revised：2025-02-09
  • Accepted：2025-02-23
Funding
State Key Research and Development Program(2017YFB1201003)
China Postdoctoral Science Foundation(2024M752470)
Qinghai Provincial Science and Technology Project(2024-HN-P01)
Affiliations
    1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
    2.State Grid Hubei Electric Power Co., Ltd., Huangshi Power Supply Company, Huangshi 435000, China
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