Lithium-ion battery remaining useful life prediction: a federated learning-based approach-Reference-Cited by-同舟云学术

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Lithium-ion battery remaining useful life prediction: a federated learning-based approach

Published:2024-04-17 Issue: Volume: Page:
ISSN:2363-7692
Container-title:Energy, Ecology and Environment
language:en
Short-container-title:Energ. Ecol. Environ.

Author:

Zhong Ruirui,Hu Bingtao,Feng Yixiong,Lou Shanhe,Hong Zhaoxi,Wang Fei,Li Guangshen,Tan Jianrong

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40974-024-00323-x.pdf

Reference45 articles.

1. Ahmad T, Madonski R, Zhang D et al (2022) Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renew Sustain Energy Rev 160:112128. https://doi.org/10.1016/j.rser.2022.112128

2. Altinpulluk NB, Altinpulluk D, Ramanan P, et al. (2023) Federated battery diagnosis and prognosis. arXiv preprint arXiv:2310.09628https://doi.org/10.48550/arXiv.2310.09628

3. Ansari S, Ayob A, Lipu MH et al (2022) Remaining useful life prediction for lithium-ion battery storage system: a comprehensive review of methods, key factors, issues and future outlook. Energy Rep 8:12153–12185. https://doi.org/10.1016/j.egyr.2022.09.043

4. Arunan A, Qin Y, Li X et al (2023) A federated learning-based industrial health prognostics for heterogeneous edge devices using matched feature extraction. IEEE Trans Autom Sci Eng. https://doi.org/10.1109/TASE.2023.3274648

5. Bai G, Su Y, Rahman MM et al (2023) Prognostics of lithium-ion batteries using knowledge-constrained machine learning and Kalman filtering. Reliab Eng Syst Saf 231:108944. https://doi.org/10.1016/j.ress.2022.108944

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