Next Article in Journal
A Method for Estimating the SOH of Lithium-Ion Batteries Based on Graph Perceptual Neural Network
Next Article in Special Issue
Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms
Previous Article in Journal
State of Health Estimation for Lithium-Ion Battery Using Partial Incremental Capacity Curve and Transfer Learning
Previous Article in Special Issue
Pyrrolidinium-Based Ionic Liquids as Advanced Non-Aqueous Electrolytes for Safer Next Generation Lithium Batteries
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Batteries
Volume 10
Issue 9
10.3390/batteries10090325
batteries-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Multi-Parameter Fusion Early Warning Method for Lithium Battery Thermal Runaway Based on Cloud Model and Dempster–Shafer Evidence Theory

by
Ziyi Xie
1,†,
Ying Zhang
1,†,
Hong Wang
1,
Pan Li
2,
Jingyi Shi
3,
Xiankai Zhang
3,* and
Siyang Li
1
1
School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China
2
Wuhan Second Institute of Ship Design and Research, Wuhan 430205, China
3
EVE Power Co., Ltd., Jingmen 516006, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Batteries 2024, 10(9), 325; https://doi.org/10.3390/batteries10090325
Submission received: 30 April 2024 / Revised: 28 August 2024 / Accepted: 12 September 2024 / Published: 13 September 2024
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire)
Browse Figures
Versions Notes

Abstract

As the preferred technology in the current energy storage field, lithium-ion batteries cannot completely eliminate the occurrence of thermal runaway (TR) accidents. It is of significant importance to employ real-time monitoring and warning methods to perceive the battery’s safety status promptly and address potential safety hazards. Currently, the monitoring and warning of lithium-ion battery TR heavily rely on the judgment of single parameters, leading to a high false alarm rate. The application of multi-parameter early warning methods based on data fusion remains underutilized. To address this issue, the evaluation of lithium-ion battery safety status was conducted using the cloud model to characterize fuzziness and Dempster–Shafer (DS) evidence theory for evidence fusion, comprehensively assessing the TR risk level. The research determined warning threshold ranges and risk levels by monitoring voltage, temperature, and gas indicators during lithium-ion battery overcharge TR experiments. Subsequently, a multi-parameter fusion approach combining cloud model and DS evidence theory was utilized to confirm the risk status of the battery at any given moment. This method takes into account the fuzziness and uncertainty among multiple parameters, enabling an objective assessment of the TR risk level of lithium-ion batteries.
Keywords: lithium-ion battery; thermal runaway; early warning; multi-source data fusion lithium-ion battery; thermal runaway; early warning; multi-source data fusion
Graphical Abstract

Share and Cite

MDPI and ACS Style

Xie, Z.; Zhang, Y.; Wang, H.; Li, P.; Shi, J.; Zhang, X.; Li, S. The Multi-Parameter Fusion Early Warning Method for Lithium Battery Thermal Runaway Based on Cloud Model and Dempster–Shafer Evidence Theory. Batteries 2024, 10, 325. https://doi.org/10.3390/batteries10090325

AMA Style

Xie Z, Zhang Y, Wang H, Li P, Shi J, Zhang X, Li S. The Multi-Parameter Fusion Early Warning Method for Lithium Battery Thermal Runaway Based on Cloud Model and Dempster–Shafer Evidence Theory. Batteries. 2024; 10(9):325. https://doi.org/10.3390/batteries10090325

Chicago/Turabian Style

Xie, Ziyi, Ying Zhang, Hong Wang, Pan Li, Jingyi Shi, Xiankai Zhang, and Siyang Li. 2024. "The Multi-Parameter Fusion Early Warning Method for Lithium Battery Thermal Runaway Based on Cloud Model and Dempster–Shafer Evidence Theory" Batteries 10, no. 9: 325. https://doi.org/10.3390/batteries10090325

APA Style

Xie, Z., Zhang, Y., Wang, H., Li, P., Shi, J., Zhang, X., & Li, S. (2024). The Multi-Parameter Fusion Early Warning Method for Lithium Battery Thermal Runaway Based on Cloud Model and Dempster–Shafer Evidence Theory. Batteries, 10(9), 325. https://doi.org/10.3390/batteries10090325

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop