Research on Multi-level Fault Warning Method for Lithium-ion Batteries Driven by Cloud Data

被引：0
|
作者：
Guo W. [1 ]
Yang L. [1 ]
Deng Z. [2 ]
Li J. [1 ]
Fan Z. [3 ]
机构：
[1] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai
[2] School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu
[3] Zhongtong Bus Co. ，Ltd., Liaocheng
来源：
Qiche Gongcheng/Automotive Engineering | 2023年 / 45卷 / 09期
关键词：
differential entropy feature sets; lithium-ion battery; unsupervised fault warning; warning level division;
D O I：
10.19562/j.chinasae.qcgc.2023.09.016
中图分类号：
学科分类号：
摘要：
At present，there is no effective method for unsupervised fault warning for vehicle cloud data with unspecified fault types. Therefore，this paper proposes a multi-level fault warning method for lithium-ion batteries driven by cloud data. Firstly，the features suitable for the characteristics of cloud data are selected through mechanism analysis，and six types of differential entropy feature sets are constructed for multiple mixed clustering to achieve the score evaluation of battery health. Then，temperature information is introduced in to distinguish heat-related faults and the warning level division criteria are constructed to determine the battery fault status. Finally，five field failure cases are used for validation. The results show that the method can accurately identify faults and distinguish fault types，and is ahead of its time and highly adaptable. © 2023 SAE-China. All rights reserved.
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页码：1677 / 1687and1701
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