Yuan Chen
A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction.
Chen, Yuan; Duan, Wenxian; He, Yigang; Wang, Shunli; Fernandez, Carlos
Authors
Abstract
Battery life prediction is of great significance to the safe operation, and the maintenance costs are reduced. This paper proposed a hybrid framework considering feature extraction to solve the problem of data backward, large sample data and uneven distribution of high-dimensional feature space, then to achieve a more accurate and stable prediction performance. By feature extraction, the measured data can be directly fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm. Better parameters of the estimation model are obtained by introducing elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The comparison is conducted by dataset from National Aeronautics and Space Administration, which shows that the proposed framework has a more accurate and stable prediction performance.
Citation
CHEN, Y., DUAN, W., HE, Y., WANG, S. and FERNANDEZ, C. 2024. A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction. Green energy and intelligent transportation [online], 3(2), article number 100160. Available from: https://doi.org/10.1016/j.geits.2024.100160
Journal Article Type | Article |
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Acceptance Date | Sep 27, 2023 |
Online Publication Date | Jan 10, 2024 |
Publication Date | Apr 30, 2024 |
Deposit Date | Jan 15, 2024 |
Publicly Available Date | Jan 15, 2024 |
Journal | Green energy and intelligent transportation |
Print ISSN | 2097-2512 |
Electronic ISSN | 2773-1537 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 3 |
Issue | 2 |
Article Number | 100160 |
DOI | https://doi.org/10.1016/j.geits.2024.100160 |
Keywords | State of heath; Improved sparrow search algorithm; Remaining useful life; Variational mode decomposition; Multi-kernel support vector regression; Feature extraction |
Public URL | https://rgu-repository.worktribe.com/output/2204905 |
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
© 2024 Published by Elsevier Ltd on behalf of Beijing Institute of Technology Press Co., Ltd.
Version
Final VOR version uploaded 2024.04.12