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Prediction of Remaining Useful Life of Lithium-ion Battery Based on Source-domain Battery Iterations Module and LSTM Neural Network
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Zipeng WANG  Student Member, CPSS1, 2, Kai HUANG  Member, CPSS1, 2, Kai SUN1, 2, Senmao Li1, 2
Journal of Power Supply | 2025, 23(7) : 294 - 303
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Journal of Power Supply | 2025, 23(7): 294-303
• Battery and Energy Storage •
Prediction of Remaining Useful Life of Lithium-ion Battery Based on Source-domain Battery Iterations Module and LSTM Neural Network
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Zipeng WANG  Student Member, CPSS1, 2, Kai HUANG  Member, CPSS1, 2, Kai SUN1, 2, Senmao Li1, 2
Affiliations
  • 1.State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
  • 2.Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China
Published: 2025-11-30 doi: 10.13234/j.issn.2095-2805.2025.7.294
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The remaining useful life (RUL) prediction of lithium-ion batteries is an important part of battery health management. To improve the accuracy of RUL prediction, a battery RUL prediction method based on the fusion of source-domain battery iterations module (SIM) and long short-term memory (LSTM) neural network is proposed. First, multiple batteries with known degradation trends are used as source-domain batteries, and their full-life capacity degradation data is used to construct an SIM to obtain the optimal LSTM pre-training model. Second, the pre-training model is transferred to the target-domain and further fine-tuned using the target-domain training set. Finally, the fine-tuned LSTM pre-training model is used for the capacity prediction task of the target-domain battery and completes the RUL prediction. Three open-source datasets were used to validate the effectiveness of the algorithm. Results show that the RUL prediction error of this algorithm is less than 2 cycles for the same type of source-domain and target-domain batteries. For different battery types, the RUL prediction error is less than 10 cycles except for 20% of the starting points for prediction, indicating a high level of prediction accuracy.

Lithium-ion battery  /  remaining useful life (RUL)  /  transfer learning  /  long short-term memory (LSTM) neural network  /  source-domain battery iterations module (SIM)
王子鹏中国电源学会学生会员, 黄凯中国电源学会会员, 孙恺, 李森茂. 基于源域电池迭代模块和LSTM神经网络的锂离子电池寿命预测. 电源学报, 2025 , 23 (7) : 294 -303 . DOI: 10.13234/j.issn.2095-2805.2025.7.294
Zipeng WANG, Kai HUANG, Kai SUN, Senmao Li. Prediction of Remaining Useful Life of Lithium-ion Battery Based on Source-domain Battery Iterations Module and LSTM Neural Network[J]. Journal of Power Supply, 2025 , 23 (7) : 294 -303 . DOI: 10.13234/j.issn.2095-2805.2025.7.294
Funding
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  • Natural Foundation of Hebei Province(E2019202328)
Appendix
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Year 2025 volume 23 Issue 7
51
8
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.7.294
  • Receive Date：2022-10-18
  • Online Date：2026-10-26
  • Published：2025-11-30
Article Data
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History
  • Received：2022-10-18
  • Revised：2022-12-29
  • Accepted：2023-02-09
Funding
Natural Foundation of Hebei Province(E2019202328)
Affiliations
    1.State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
    2.Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China
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