Deep transfer learning enables battery state of charge and state of health estimation-Reference-Cited by-同舟云学术

Deep transfer learning enables battery state of charge and state of health estimation

Published:2024-05 Issue: Volume:294 Page:130779
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Yang Yongsong,Xu Yuchen^ORCID,Nie Yuwei,Li Jianming,Liu Shizhuo,Zhao Lijun,Yu Quanqing^ORCID,Zhang Chengming^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference28 articles.

1. Evaluation of the safety standards system of power batteries for electric vehicles in China;Yu;Appl Energy,2023

2. Targeting the low-temperature performance degradation of lithium-ion batteries: a non-destructive bidirectional pulse current heating framework;Huang;Energy Storage Mater,2024

3. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles;Xiong;Appl Energy,2020

4. Online health prognosis for lithium-ion batteries under dynamic discharge conditions over wide temperature range;Liu;eTransportation,2023

5. An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range;Jiang;Energy,2023

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1. Machine learning enables rapid state of health estimation of each cell within battery pack;Applied Energy;2024-12

2. State of charge estimation of lithium batteries in wide temperature range based on MSIABC-AEKF algorithm;Alexandria Engineering Journal;2024-12

3. Online state of health estimation for Li-ion batteries in EVs through a data-fusion-model method;Journal of Energy Storage;2024-10