Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization-Reference-Cited by-同舟云学术

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Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization

Published:2024-09-13 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Rao Zhibo,Wu Jiahui,Li Guodong,Wang Haiyun

Funder

Project supported by the Open project of Key Laboratory in Xinjiang. Uygur Autonomous Region of China

National Natural Science Foundation of China

Project Supported by Key Research. and Development Project of Xinjiang Uygur Autonomous Region

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-72510-z.pdf

Reference40 articles.

1. Zhang, Q. et al. State-of-health estimation of batteries in an energy storage system based on the actual operating parameters. J. Power Sources 506, 230162. https://doi.org/10.1016/j.jpowsour.2021.230162 (2021).

2. Pradhan, S. & Chakraborty, B. Battery management strategies: An essential review for battery state of health monitoring techniques. J. Energy Storage https://doi.org/10.1016/j.est.2022.104427 (2022).

3. Gong, D. et al. State of health estimation for lithium-ion battery based on energy features. Energy 257, 124812 (2022).

4. Lingyun, L. I. Current situation and development trend of lithium battery industry for new energy vehicles in China. Power Technol. 44(4), 628–630 (2020).

5. Yin, H. et al. Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse. ETransportation 7, 100098 (2021).

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