A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries

Shunli Wang*, Siyu Jin, Dekui Bai, Yongcun Fan, Haotian Shi, Carlos Fernandez

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

87 Citations (Scopus)
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As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries.

Original languageEnglish
JournalEnergy Reports
Pages (from-to)5562-5574
Number of pages13
Publication statusPublished - Nov 2021

Bibliographical note

Funding Information:
The work was supported by the National Natural Science Foundation of China (No. 62173281 , 61801407 ), Sichuan science and technology program (No. 2019YFG0427 ), China Scholarship Council (No. 201908515099 ), and Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province (No. 18kftk03 ). Thanks to the sponsors.

Publisher Copyright:
© 2021


  • Deep convolutional neural network
  • Deep learning
  • Lithium-ion battery
  • Long short term memory
  • Recurrent neural network
  • Remaining useful life prediction


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