FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data-Reference-Cited by-同舟云学术

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FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data

Published:2024-08 Issue: Volume:368 Page:123534
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Lai Rucong^ORCID,Wang Jie^ORCID,Tian Yong,Tian Jindong^ORCID

Publisher

Elsevier BV

Reference36 articles.

1. China’s battery electric vehicles lead the world: Achievements in technology system architecture and technological breakthroughs;He;Green Energy Intell Transp,2022

2. Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey;Yang;J Energy Storage,2021

3. A novel Pseudo-open-circuit voltage modeling method for accurate state-of-charge estimation of LiFePO4 batteries;Zhang;Appl Energy,2023

4. Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles;Hong;J Energy Storage,2020

5. A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures;Cui;Renew Energy,2022

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