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Remaining Useful Life Prediction Algorithm for Lithium-ion Batteries Based on LSTM of Capacity Sequence Decomposition and Reconstruction
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Jie FENG  Student Member, CPSS1, Zhigang LÜ1, Ruohai DI1, Peng WANG2, Chu WANG1
Journal of Power Supply | 2026, 24(3) : 230 - 241
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Journal of Power Supply | 2026, 24(3): 230-241
• AI and Power Electronics •
Remaining Useful Life Prediction Algorithm for Lithium-ion Batteries Based on LSTM of Capacity Sequence Decomposition and Reconstruction
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Jie FENG  Student Member, CPSS1, Zhigang LÜ1, Ruohai DI1, Peng WANG2, Chu WANG1
Affiliations
  • 1.School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China
  • 2.Development and Planning Department, Xi’an Technological University, Xi’an 710021, China
Published: 2026-03-30 doi: 10.13234/j.issn.2095-2805.2026.3.230
Outline
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The accurate prediction of the futural capacity of lithium batteries is crucial for their health management systems. Aimed at the impact of capacity regeneration on the capacity sequences of lithium-ion batteries, a long short-term memory (LSTM) neural network-based remaining useful life prediction model is proposed, which is based on capacity sequence decomposition and reconstruction. First, the CEEMDAN technology is used to decompose the original sequence into various intrinsic mode components, so as to eliminate the phenomenon of capacity rebound. Second, the Hurst technique is utilized to reassemble each component into multiple systematically strong sub-sequences, which can reduce the time consumption during the testing phase. Third, the whale optimization algorithm is applied to multiple sub-sequences to optimize the parameters of LSTM, and the outcomes from various sets of predictions are further aggregated to derive the ultimate prediction results. Finally, the effectiveness of the proposed model is verified on two sets of lithium-ion battery failure datasets with different degradation characteristics. Experimental results show that the proposed method can maintain both the mean absolute error and root mean square error in certain ranges.

Lithium-ion battery  /  empirical mode decomposition  /  Hurst  /  whale optimization algorithm  /  long short-term memory (LSTM) neural network
冯杰中国电源学会学生会员, 吕志刚, 邸若海, 王鹏, 王储. 基于容量序列分解与重构的LSTM锂电池剩余寿命预测算法. 电源学报, 2026 , 24 (3) : 230 -241 . DOI: 10.13234/j.issn.2095-2805.2026.3.230
Jie FENG, Zhigang LÜ, Ruohai DI, Peng WANG, Chu WANG. Remaining Useful Life Prediction Algorithm for Lithium-ion Batteries Based on LSTM of Capacity Sequence Decomposition and Reconstruction[J]. Journal of Power Supply, 2026 , 24 (3) : 230 -241 . DOI: 10.13234/j.issn.2095-2805.2026.3.230
Funding
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  • National Natural Science Foundation of China(62171360)
  • Shaanxi Science and Technology Department(2023-LL-QY-25)
  • National Key Research and Development Program(2022YFF0604900)
  • 2022 Shaanxi University Youth Innovation Team Project
  • Shandong Key Laboratory of Smart Transportation (Preparation)
  • 2023 Shaanxi University Engineering Research Center
Appendix
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Year 2026 volume 24 Issue 3
28
4
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.3.230
  • Receive Date：2023-10-21
  • Online Date：2026-10-29
  • Published：2026-03-30
Article Data
Affiliations
History
  • Received：2023-10-21
  • Revised：2023-12-09
  • Accepted：2023-12-25
Funding
National Natural Science Foundation of China(62171360)
Shaanxi Science and Technology Department(2023-LL-QY-25)
National Key Research and Development Program(2022YFF0604900)
2022 Shaanxi University Youth Innovation Team Project
Shandong Key Laboratory of Smart Transportation (Preparation)
2023 Shaanxi University Engineering Research Center
Affiliations
    1.School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China
    2.Development and Planning Department, Xi’an Technological University, Xi’an 710021, 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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