An accurate denoising lithium-ion battery remaining useful life prediction model based on CNN and LSTM with self-attention
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Engineering,General Materials Science,General Chemical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11581-023-05204-7.pdf
Reference43 articles.
1. Cui RG, Guo J, Cheng LH, Zhang YX, Liu W (2021) Status and trends analysis of global clean energies. Acta Geosci Sin 42:179–186. https://doi.org/10.3975/cagsb.2020.090401
2. Liu Q, Hao SY, Feng D, Mei Y, Zeng TB (2022) Research progress of anode materials for lithium-ion battery. Acta Mater Compos Sin 39:1446–1456. https://doi.org/10.13801/j.cnki.fhclxb.20211101.002
3. Chen W, Liang J, Yang Z, Li G (2019) A review of lithium-ion battery for electric vehicle applications and beyond. Energy Procedia 158:4363–4368. https://doi.org/10.1016/j.egypro.2019.01.783
4. Li X, Yu D, Vilsen SB, Store DI (2023) The development of machine learning-based remaining useful life prediction for lithium-ion batteries. J Energy Chem. https://doi.org/10.1016/j.jechem.2023.03.026
5. Kong JZ, Yang F, Zhang X, Pan E, Peng Z, Wang D (2021) Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries. Energy 223:120114. https://doi.org/10.1016/j.energy.2021.120114
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