State of health estimation for lithium-ion batteries using a hybrid neural network model with Multi-scale Convolutional Attention Mechanism-Reference-Cited by-同舟云学术

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State of health estimation for lithium-ion batteries using a hybrid neural network model with Multi-scale Convolutional Attention Mechanism

Published:2024-07 Issue: Volume:609 Page:234680
ISSN:0378-7753
Container-title:Journal of Power Sources
language:en
Short-container-title:Journal of Power Sources

Author:

He Tao^ORCID,Gong Ziyang^ORCID

Publisher

Elsevier BV

Reference43 articles.

1. Ensemble method with heterogeneous models for battery state-of-health estimation;Lin;IEEE Trans. Ind. Inf.,2023

2. Machine learning‐based model for lithium‐ion batteries in BMS of electric/hybrid electric aircraft;Hashemi;Int. J. Energy Res.,2021

3. A review of lithium-ion battery state of health estimation and prediction methods;Yao;WEVJ,2021

4. Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm;Feng;J. Energy Storage,2018

5. Review on state-of-health of lithium-ion batteries: characterizations, estimations and applications;Yang;J. Clean. Prod.,2021

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integrated Mixed Attention U-Net Mechanisms with Multi-Stage Division Strategy Customized for Accurate Estimation of Lithium-Ion Battery State of Health;Electronics;2024-08-15

2. Estimating the Health State of Lithium-Ion Batteries Using an Adaptive Gated Sequence Network and Hierarchical Feature Construction;Batteries;2024-08-03

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4. Interpreting Geometric Constructions in Artworks through Capsule Network Modeling;Applied Mathematics and Nonlinear Sciences;2024-01-01

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