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Estimation of State-of-health for Cascaded Utilized Lithium Batteries Based on Pulse Voltage and CNN-GRU
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Chuankai YANG1, Haolu LIU1, Yichen YANG1, Yu FAN2, Huiying ZHANG  Student Member, CPSS3, Jianwei SHAO  Senior Member, CPSS3
Journal of Power Supply | 2026, 24(4) : 178 - 187
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Journal of Power Supply | 2026, 24(4): 178-187
• Renewable Energy Generation and Energy Storage •
Estimation of State-of-health for Cascaded Utilized Lithium Batteries Based on Pulse Voltage and CNN-GRU
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Chuankai YANG1, Haolu LIU1, Yichen YANG1, Yu FAN2, Huiying ZHANG  Student Member, CPSS3, Jianwei SHAO  Senior Member, CPSS3
Affiliations
  • 1.Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710100, China
  • 2.Shaanxi Daqin Electric Energy Group Co., Ltd., Xi’an 710004, China
  • 3.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Published: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.178
Outline
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Aimed at the difficulties and lack of precision in the online estimation of state-of-health (SOH) for cascaded utilized lithium batteries, an estimation method based on pulse voltage and hybrid neural network is proposed. Based on the battery pulse voltage response curve, this method extracts the slope of terminal voltage and the corrected standard deviation which are related to battery SOH degradation as health features. By integrating convolutional neural networks (CNN) and gated recurrent unit (GRU), the online estimation of SOH for cascaded utilized lithium batteries with high accuracy and high stability is realized. A platform for a battery testing system was established to validate the proposed SOH estimation strategy for cascaded utilized lithium batteries. Results indicate that the proposed method excels in real-time accurate estimation of lithium battery SOH, and both the average absolute error and root mean square error are controlled within 0.01, demonstrating excellent generalization capability and robustness.

Cascaded utilized lithium battery  /  state-of-health (SOH)  /  pulse voltage  /  convolutional neural networks-gated recurrent unit (CNN-GRU)
杨传凯, 刘皓璐, 杨毅琛, 樊玉, 张慧颖中国电源学会学生会员, 邵建伟中国电源学会高级会员. 基于脉冲电压和CNN-GRU的梯次利用锂电池健康状态估计. 电源学报, 2026 , 24 (4) : 178 -187 . DOI: 10.13234/j.issn.2095-2805.2026.4.178
Chuankai YANG, Haolu LIU, Yichen YANG, Yu FAN, Huiying ZHANG, Jianwei SHAO. Estimation of State-of-health for Cascaded Utilized Lithium Batteries Based on Pulse Voltage and CNN-GRU[J]. Journal of Power Supply, 2026 , 24 (4) : 178 -187 . DOI: 10.13234/j.issn.2095-2805.2026.4.178
Funding
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  • State Key Research and Development Program(2017YFB1201003)
  • Science and Technology Program of State Grid Shaanxi Electric Power Co., Ltd.(5226KY23000P)
Appendix
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Year 2026 volume 24 Issue 4
31
6
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.4.178
  • Receive Date：2024-01-18
  • Online Date：2026-10-29
  • Published：2026-04-30
Article Data
Affiliations
History
  • Received：2024-01-18
  • Revised：2024-03-25
  • Accepted：2024-04-07
Funding
State Key Research and Development Program(2017YFB1201003)
Science and Technology Program of State Grid Shaanxi Electric Power Co., Ltd.(5226KY23000P)
Affiliations
    1.Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710100, China
    2.Shaanxi Daqin Electric Energy Group Co., Ltd., Xi’an 710004, China
    3.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, 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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