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Remaining Useful Life Prediction of a Lithium–Ion Battery Based on a Temporal Convolutional Network with Data Extension Cover

Remaining Useful Life Prediction of a Lithium–Ion Battery Based on a Temporal Convolutional Network with Data Extension

International Journal of Applied Mathematics and Computer Science
Volume 34 (2024): Issue 1 (March 2024)
By: Jing Zhao,  Dayong Liu and  Lingshuai Meng  
Open Access
|Mar 2024

Authors

Jing Zhao

liudayong@sia.cn

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Institute of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China

Dayong Liu

zhaojing@sia.cn

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
University of the Chinese Academy of Sciences, Beijing, China

Lingshuai Meng

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
University of the Chinese Academy of Sciences, Beijing, China
DOI: https://doi.org/10.61822/amcs-2024-0008 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 105 - 117
Submitted on: Mar 15, 2023
Accepted on: Nov 13, 2023
Published on: Mar 26, 2024
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
lithium-ion battery,
remaining useful life,
complete EEMD with adaptive noise,
temporal convolutional net
Related subjects:
Mathematics,
Applied mathematics

© 2024 Jing Zhao, Dayong Liu, Lingshuai Meng, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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