Remaining Useful Life Prediction of a Lithium–Ion Battery Based on a Temporal Convolutional Network with Data Extension
By:
Authors
Jing Zhao
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Institute of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
Dayong Liu
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
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 times per year
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© 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.