Improved K-means clustering-genetic backpropagation modeling for online state-of-charge estimation of lithium-ion batteries adaptive to low-temperature conditions-Reference-Cited by-同舟云学术

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Improved K-means clustering-genetic backpropagation modeling for online state-of-charge estimation of lithium-ion batteries adaptive to low-temperature conditions

Published:2024-10 Issue: Volume:99 Page:113399
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Hai Nan,Wang Shunli,Huang Qi,Xie Yanxin,Fernandez Carlos

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference50 articles.

1. Application of microdiverse carbon materials and loaded binary metals in lithium-ion capacitors;Shi;Journal of Energy Storage,2023

2. State of charge estimation for a parallel battery pack jointly by fuzzy-PI model regulator and adaptive unscented Kalman filter;Peng;Appl. Energy,2024

3. Flexible solid-state lithium-ion batteries: materials and structures;Deng;Energies,2023

4. Study on the estimation of the state of charge of lithium-ion battery;Yuan;Electrochim. Acta,2024

5. State of charge estimation of lithium-ion battery based on GA-LSTM and improved IAKF;Wang;Appl. Energy,2024

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