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

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
• Electronic 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