A hybrid kernel extreme learning machine modeling method based on improved dung beetle algorithm optimization for lithium-ion battery state of health estimation-Reference-Cited by-同舟云学术

A hybrid kernel extreme learning machine modeling method based on improved dung beetle algorithm optimization for lithium-ion battery state of health estimation

Published:2024-05-16 Issue:7 Volume:30 Page:3995-4009
ISSN:0947-7047
Container-title:Ionics
language:en
Short-container-title:Ionics

Author:

Mo Daijiang,Wang Shunli,Zhang Mengyun,Fan Yongcun,Wang Yangtao,Zeng Jiawei

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11581-024-05573-7.pdf

Reference47 articles.

1. Zhou Y, Wang S, Xie Y, Shen X, Fernandez C (2023) Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm. Energy 285:128761. https://doi.org/10.1016/j.energy.2023.128761

2. Zheng L, Zhu J, Lu DD-C, Wang G, He T (2018) Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries. Energy 150:759–769. https://doi.org/10.1016/j.energy.2018.03.023

3. Takyi-Aninakwa P, Wang S, Zhang H, Yang X, Fernandez C (2023) A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures. Energy 273:127231. https://doi.org/10.1016/j.energy.2023.127231

4. Yang X, Wang S, Takyi-Aninakwa P, Yang X, Fernandez C (2023) Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference. J Energy Storage 73:108974. https://doi.org/10.1016/j.est.2023.108974

5. Li X, Lyu M, Li K, Gao X, Liu C, Zhang Z (2023) Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning. Energy 282:128445. https://doi.org/10.1016/j.energy.2023.128445