{"title":"Lithium Battery Thermal-Runaway Monitoring Based on Whole-Feature Neural Networks","authors":"Zhichen Liu, Ying Li","doi":"10.1149/1945-7111/ad69c5","DOIUrl":null,"url":null,"abstract":"Thermal runaway monitoring and analysis has become a serious challenge to the safety of lithium-ion battery driven electric equipment. Thermal-runaway monitoring is crucial to avoid the burning and explosion of lithium batteries. This paper proposes a new type of deep neural network, known as whole-feature neural networks (WFNN), for lithium battery thermal-runaway monitoring. The neural networks learn the thermal-runaway patterns of a lithium battery from the measured temperatures, current, and voltages. WFNN is an end-to-end model for thermal-runaway monitoring of lithium batteries. An experiment on thermal-runaway monitoring of lithium batteries was carried out to evaluate the performance of the proposed WFNN. The monitoring accuracy is up to 99.48%, which is higher than those of support vector machine, kernel support vector machine, k-nearest neighbor, and fully-connected neural networks. Moreover, the computation efficiency of WFNN is high enough for real-time thermal-runaway monitoring. As a result, experimental results show that the proposed WFNN is applicable to the thermal-runaway monitoring of lithium batteries.","PeriodicalId":17364,"journal":{"name":"Journal of The Electrochemical Society","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Electrochemical Society","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1149/1945-7111/ad69c5","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ELECTROCHEMISTRY","Score":null,"Total":0}
引用次数: 0
Abstract
Thermal runaway monitoring and analysis has become a serious challenge to the safety of lithium-ion battery driven electric equipment. Thermal-runaway monitoring is crucial to avoid the burning and explosion of lithium batteries. This paper proposes a new type of deep neural network, known as whole-feature neural networks (WFNN), for lithium battery thermal-runaway monitoring. The neural networks learn the thermal-runaway patterns of a lithium battery from the measured temperatures, current, and voltages. WFNN is an end-to-end model for thermal-runaway monitoring of lithium batteries. An experiment on thermal-runaway monitoring of lithium batteries was carried out to evaluate the performance of the proposed WFNN. The monitoring accuracy is up to 99.48%, which is higher than those of support vector machine, kernel support vector machine, k-nearest neighbor, and fully-connected neural networks. Moreover, the computation efficiency of WFNN is high enough for real-time thermal-runaway monitoring. As a result, experimental results show that the proposed WFNN is applicable to the thermal-runaway monitoring of lithium batteries.
期刊介绍:
The Journal of The Electrochemical Society (JES) is the leader in the field of solid-state and electrochemical science and technology. This peer-reviewed journal publishes an average of 450 pages of 70 articles each month. Articles are posted online, with a monthly paper edition following electronic publication. The ECS membership benefits package includes access to the electronic edition of this journal.