AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (5.4 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Review | Open Access

A Critical Review of Thermal Runaway Prediction and Early-Warning Methods for Lithium-Ion Batteries

Xi Zhang( ), Shun Chen, Jingzhe Zhu, Yizhao Gao( )
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China
Show Author Information

Abstract

Lithium-ion batteries are widely used in electric vehicles because of their high energy density and long cycle life. However, the spontaneous combustion accident of electric vehicles caused by thermal runaway of lithium-ion batteries seriously threatens passengers' personal and property safety. This paper expounds on the internal mechanism of lithium-ion battery thermal runaway through many previous studies and summarizes the proposed lithium-ion battery thermal runaway prediction and early warning methods. These methods can be classified into battery electrochemistry-based, battery big data analysis, and artificial intelligence methods. In this paper, various lithium-ion thermal runaway prediction and early warning methods are analyzed in detail, including the advantages and disadvantages of each method, and the challenges and future development directions of the intelligent lithium-ion battery thermal runaway prediction and early warning methods are discussed.

References

【1】
【1】
 
 
Energy Material Advances
Article number: 0008

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Zhang X, Chen S, Zhu J, et al. A Critical Review of Thermal Runaway Prediction and Early-Warning Methods for Lithium-Ion Batteries. Energy Material Advances, 2023, 4: 0008. https://doi.org/10.34133/energymatadv.0008

4764

Views

145

Downloads

168

Crossref

127

Web of Science

165

Scopus

0

CSCD

Received: 25 September 2022
Accepted: 18 November 2022
Published: 10 January 2023
© 2023 Xi Zhang et al. Exclusive Licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).