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Estimation Method for Battery Capacity Based on Multi-health Factor LSTM
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Yimin MO1, Jiahao CHEN1, Peng YE1, Wenjian FAN2, Jun WEI2, Jingyu SUN1
Journal of Power Supply | 2026, 24(1) : 207 - 216
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Journal of Power Supply | 2026, 24(1): 207-216
• Renewable Energy Generation and Energy Storage •
Estimation Method for Battery Capacity Based on Multi-health Factor LSTM
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Yimin MO1, Jiahao CHEN1, Peng YE1, Wenjian FAN2, Jun WEI2, Jingyu SUN1
Affiliations
  • 1.School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
  • 2.SAIC-GM-Wuling Automobile Co., Ltd., Liuzhou 545007, China
Published: 2026-01-30 doi: 10.13234/j.issn.2095-2805.2026.1.207
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To accurately determine the battery capacity, long short-term memory (LSTM) neural network was used to estimate the battery capacity. First, the life-cycle curves for different battery parameters were analyzed. The Pearson correlation coefficients between the parameters and battery capacity were calculated, and the battery voltage, internal resistance, equal voltage drop time and other parameters were selected as health factors to construct a battery capacity estimation model. Then, the Center for Advanced Life Cycle Engineering (CALCE) battery data set was used for model training and capacity estimation, and the average percentage error of the estimation model was 1.19%. The causes of estimation errors were analyzed, and the model was optimized by revising the battery’s initial capacity and aging parameters. The optimization results show that the estimation error of the model constructed using battery voltage, internal resistance, constant-current charging time and 4.0–3.4 V equal voltage drop time was about 0.55%.

Capacity estimation  /  battery state-of-health  /  long short-term memory (LSTM) neural network  /  lithium-ion battery
莫易敏, 陈佳浩, 叶鹏, 范文健, 韦军, 孙静宇. 基于多健康因子LSTM的电池容量估计方法. 电源学报, 2026 , 24 (1) : 207 -216 . DOI: 10.13234/j.issn.2095-2805.2026.1.207
Yimin MO, Jiahao CHEN, Peng YE, Wenjian FAN, Jun WEI, Jingyu SUN. Estimation Method for Battery Capacity Based on Multi-health Factor LSTM[J]. Journal of Power Supply, 2026 , 24 (1) : 207 -216 . DOI: 10.13234/j.issn.2095-2805.2026.1.207
Appendix
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Year 2026 volume 24 Issue 1
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.1.207
  • Receive Date：2023-03-20
  • Online Date：2026-10-26
  • Published：2026-01-30
Article Data
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History
  • Received：2023-03-20
  • Revised：2023-05-12
  • Accepted：2023-05-20
Affiliations
    1.School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
    2.SAIC-GM-Wuling Automobile Co., Ltd., Liuzhou 545007, China
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