Xinmiao Liang , Peng Wang , Xi Cao , Xinming Wan , Peipei Chao , Xing Zhao , Andong Yu , Chuan Liu , Jiale Li
{"title":"Research on improving the safety of new energy vehicles exploits vehicle operating data","authors":"Xinmiao Liang , Peng Wang , Xi Cao , Xinming Wan , Peipei Chao , Xing Zhao , Andong Yu , Chuan Liu , Jiale Li","doi":"10.1016/j.ssci.2024.106681","DOIUrl":null,"url":null,"abstract":"<div><div>New energy vehicles (NEV), a four-wheel vehicle that employs non-traditional fuels, develops rapidly, lacking in research and application on vehicle operating data mining to improve the safety status of NEV. In this study, the method to improve the safety of new energy vehicles through vehicle operating data was researched systematically. First, known combustion accidents of NEV were counted from multiple dimensions to present the current safety situation. Subsequently, the study delves deeper into the specific causes of combustion in battery electric vehicles with lithium-ion batteries by examining parameter trends and performance discrepancies. Then, a novel approach for detecting abnormal self-discharge in power battery cells using operational data was proposed. This method aids in the early diagnosis of abnormalities and has been successfully utilized to issue advance warnings for vehicles exhibiting such issues. In conclusion, the study offers strategic recommendations for digital interventions by regulatory bodies and manufacturers. These recommendations aim to foster a robust and sustainable NEV industry, prioritizing safety and fostering innovation.</div></div>","PeriodicalId":21375,"journal":{"name":"Safety Science","volume":"181 ","pages":"Article 106681"},"PeriodicalIF":4.7000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Safety Science","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925753524002716","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
New energy vehicles (NEV), a four-wheel vehicle that employs non-traditional fuels, develops rapidly, lacking in research and application on vehicle operating data mining to improve the safety status of NEV. In this study, the method to improve the safety of new energy vehicles through vehicle operating data was researched systematically. First, known combustion accidents of NEV were counted from multiple dimensions to present the current safety situation. Subsequently, the study delves deeper into the specific causes of combustion in battery electric vehicles with lithium-ion batteries by examining parameter trends and performance discrepancies. Then, a novel approach for detecting abnormal self-discharge in power battery cells using operational data was proposed. This method aids in the early diagnosis of abnormalities and has been successfully utilized to issue advance warnings for vehicles exhibiting such issues. In conclusion, the study offers strategic recommendations for digital interventions by regulatory bodies and manufacturers. These recommendations aim to foster a robust and sustainable NEV industry, prioritizing safety and fostering innovation.
期刊介绍:
Safety Science is multidisciplinary. Its contributors and its audience range from social scientists to engineers. The journal covers the physics and engineering of safety; its social, policy and organizational aspects; the assessment, management and communication of risks; the effectiveness of control and management techniques for safety; standardization, legislation, inspection, insurance, costing aspects, human behavior and safety and the like. Papers addressing the interfaces between technology, people and organizations are especially welcome.