Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries-Reference-Cited by-同舟云学术

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Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

Published:2024-09 Issue: Volume:17 Page:100405
ISSN:2666-5468
Container-title:Energy and AI
language:en
Short-container-title:Energy and AI

Author:

Wang Ya-Xiong,Zhao Shangyu,Wang Shiquan,Ou Kai,Zhang Jiujun

Funder

Major Science and Technology Project of Hainan Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference51 articles.

1. Understanding battery aging in grid energy storage systems;Kumtepeli;Joule,2022

2. Physical-based training data collection approach for data-driven lithium-ion battery state-of-charge prediction;Li;Energy AI,2021

3. Progress and challenges in ultrasonic technology for state estimation and defect detection of lithium-ion batteries;Wang;Energy Storage Mater,2024

4. Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data;Ruan;Energy AI,2024

5. Power electronics-based safety enhancement technologies for lithium-ion batteries: an overview from battery management perspective;Zhao;IEEE Trans Power Electron,2023

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