Internal short circuit fault diagnosis for the lithium-ion batteries with unknown parameters based on transfer learning optimized residual network by multi-label data processing-Reference-Cited by-同舟云学术

Internal short circuit fault diagnosis for the lithium-ion batteries with unknown parameters based on transfer learning optimized residual network by multi-label data processing

Published:2024-03 Issue: Volume:444 Page:141224
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Sun Tao,Zhu Hao,Xu Yuwen,Jin Changyong,Zhu Guangying,Han Xuebing,Lai Xin,Zheng Yuejiu

Funder

Natural Science Foundation of Shanghai Municipality

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference26 articles.

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3. Internal short circuit early detection of lithium-ion batteries from impedance spectroscopy using deep learning;Cui;J. Power Sources,2023

4. A novel method of quantitative internal short circuit diagnosis based on charging electric quantity in fixed voltage window;Fan;J. Energy Storage,2023

5. Mitigating thermal runaway of lithium-ion batteries;Feng;Joule,2020

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