Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model-Reference-Cited by-同舟云学术

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Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model

Published:2024-07-06 Issue:9 Volume:30 Page:5449-5471
ISSN:0947-7047
Container-title:Ionics
language:en
Short-container-title:Ionics

Author:

Feng Renjun,Wang Shunli,Yu Chunmei,Fernandez Carlos

Funder

National Natural Science Foundation of China

Sichuan Science and Technology Program

Dazhou City School Cooperation Project

Technopole Talent Summit Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11581-024-05685-0.pdf

Reference45 articles.

1. Chen Z, Chen LQ, Shen WJ, Xu KK (2022) Remaining useful life prediction of lithium-ion battery via a sequence decomposition and deep learning integrated approach. IEEE Trans Veh Technol 71(2):1466–1479

2. Du ZK, Zuo L, Li JJ, Liu Y, Shen HT (2022) Data-driven estimation of remaining useful lifetime and state of charge for lithium-ion battery. IEEE Trans Transp Electrific 8(1):356–367

3. Zhang XH, Xu Y, Gong ZH (2023) A feature fusion optimization algorithm for predicting the remaining useful life of lithium-ion batteries. Energy Rep 9:142–153

4. Xia TC, Zhang X, Zhu HF, Zhang XC, Shen J (2023) An accurate denoising lithium-ion battery remaining useful life prediction model based on CNN and LSTM with self-attention. Ionics 29(12):5315–5328

5. Dong GZ, Han WJ, Wang YJ (2021) Dynamic Bayesian network-based lithium-ion battery health prognosis for electric vehicles. IEEE Trans Industr Electron 68(11):10949–10958

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