Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks-Reference-Cited by-同舟云学术

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Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks

Published:2024-08 Issue:16 Volume:10 Page:e36236
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Suh Sungho^ORCID,Mittal Dhruv Aditya,Bello Hymalai,Zhou Bo,Jha Mayank Shekhar,Lukowicz Paul

Funder

Carl Zeiss Stiftung

European Union

Publisher

Elsevier BV

Reference57 articles.

1. “Knees” in lithium-ion battery aging trajectories;Attia;J. Electrochem. Soc.,2022

2. Degradation diagnostics for lithium ion cells;Birkl;J. Power Sources,2017

3. An overview of data-driven battery health estimation technology for battery management system;Chen;Neurocomputing,2023

4. State-of-health estimation of lithium-ion batteries with attention-based deep learning;Cui,2020

5. Lithium-ion battery data and where to find it;Dos Reis;Energy AI,2021

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