An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries-Reference-Cited by-同舟云学术

An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries

Published:2023-11-09 Issue:1 Volume:30 Page:311-334
ISSN:0947-7047
Container-title:Ionics
language:en
Short-container-title:Ionics

Author:

Li Zehao,Wang Shunli,Yu Chunmei,Qi Chuangshi,Shen Xianfeng,Fernandez Carlos

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/s11581-023-05272-9.pdf

Reference51 articles.

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2. Li Z, Xiong R, Mu H et al (2017) A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles. Appl Energy 207:363–371. https://doi.org/10.1016/j.apenergy.2017.05.081

3. Wang C, Wang S, Zhou J, Qiao J (2022) A novel BCRLS-BP-EKF method for the state of charge estimation of lithium-ion batteries. Int J Electrochem Sci:220431. https://doi.org/10.20964/2022.04.53

4. Chaoui H, Mandalapu S (2017) Comparative study of online open circuit voltage estimation techniques for state of charge estimation of lithium-ion batteries. Batteries 3:12. https://doi.org/10.3390/batteries3020012

5. Yu Q, Nie Y, Peng S et al (2023) Evaluation of the safety standards system of power batteries for electric vehicles in China. Appl Energy 349:121674. https://doi.org/10.1016/j.apenergy.2023.121674