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

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

Authors

Shunli Wang

Siyu Jin

Dekui Bai

Yongcun Fan

Haotian Shi



Abstract

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.

Citation

WANG, S., JIN, S., BAI, D., FAN, Y., SHI, H. and FERNANDEZ, C. 2021. A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy reports [online], 7, pages 5562-5574. Available from: https://doi.org/10.1016/j.egyr.2021.08.182

Journal Article Type Article
Acceptance Date Aug 25, 2021
Online Publication Date Sep 9, 2021
Publication Date Nov 30, 2021
Deposit Date Sep 10, 2021
Publicly Available Date Sep 10, 2021
Journal Energy Reports
Print ISSN 2352-4847
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 7
Pages 5562-5574
DOI https://doi.org/10.1016/j.egyr.2021.08.182
Keywords Lithium-ion battery; Remaining useful life prediction; Deep learning; Deep convolutional neural network; Long short term memory; Recurrent neural network
Public URL https://rgu-repository.worktribe.com/output/1447646

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