Research on Intelligent Safety Management and Control Methods for Big-data-driven Battery Systems

被引：0
|
作者：
Hong J. [1 ,2 ]
Liang F. [1 ,2 ]
Yang H. [1 ,2 ]
Li K. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, University of Science and Technology Beijing, Beijing
[2] Shunde Innovation School, University of Science and Technology, Beijing, Foshan
来源：
Qiche Gongcheng/Automotive Engineering | 2023年 / 45卷 / 10期
关键词：
control strategy; data-driven; electric vehicles; fault diagnosis; power battery;
D O I：
10.19562/j.chinasae.qcgc.2023.10.007
中图分类号：
学科分类号：
摘要：
For the research on safety risk management and control of new energy vehicle power batteries，this paper discusses in detail the failure mechanism and types of power battery systems，clarifies the coupling relationship between battery consistency and safety based on big data statistical analysis，and summarizes the data-driven safety state prediction，fault diagnosis and warning method. Finally，a "vehicle-cloud"-integration-based safety control strategy is proposed for real-vehicle battery systems. This paper aims to provide theoretical guidance for realizing real-time monitoring of battery safety status and risk warning for real vehicles. © 2023 SAE-China. All rights reserved.
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页码：1845 / 1861and1907
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