A strong robust state-of-charge estimation method based on the gas-liquid dynamics model-Reference-Cited by-同舟云学术

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A strong robust state-of-charge estimation method based on the gas-liquid dynamics model

Published:2024-03 Issue: Volume: Page:100193
ISSN:2773-1537
Container-title:Green Energy and Intelligent Transportation
language:en
Short-container-title:Green Energy and Intelligent Transportation

Author:

Chen Biao^ORCID,Song Liang,Jiang Haobin,Zhao Zhiguo,Zhu Jun,Xu Keqiang

Funder

China Postdoctoral Science Foundation

Huaiyin Institute of Technology

Natural Science Research of Jiangsu Higher Education Institutions of China

Publisher

Elsevier BV

Reference36 articles.

1. Evaluation of the Safety Standards System of Power Batteries for Electric Vehicles in China;Yu;Applied energy,2023

2. State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges;Xiong;Applied Energy,2023

3. State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model;Pan;Energy,2017

4. A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries;Wang;Energy Reports,2021

5. State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network;Peng;Energy,2023

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