contributor author | Chang, Chun;Tao, Chen;Wang, Shaojin;Zhang, Ruhang;Tian, Aina;Jiang, Jiuchun | |
date accessioned | 2023-04-06T12:54:16Z | |
date available | 2023-04-06T12:54:16Z | |
date copyright | 10/3/2022 12:00:00 AM | |
date issued | 2022 | |
identifier issn | 23816872 | |
identifier other | jeecs_20_3_031004.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4288734 | |
description abstract | Due to the frequent occurrence of electric vehicles safety accidents caused by battery system failures, in order to ensure the normal operation of the vehicle, it is crucial to do a fault diagnosis of the electric vehicle lithium battery. This paper presents a fault diagnosis method for lithium batteries based on optimal variational modal decomposition and dimensionless feature parameters for identifying faulty batteries. The method first preprocesses the voltage signal of a lithium battery by optimal variable mode decomposition to obtain the high and lowfrequency components of the signal and reconstructs the high and lowfrequency components. Then, the dimensionless feature parameters are extracted according to the reconstructed signal, and feature reduction of the dimensionless feature parameters is carried out by a locally linear embedding algorithm. Finally, a local outlier factor algorithm is used to detect faulty batteries. After verified by the operation data before the real electric vehicle's thermal runaway failure, this method can detect the faulty battery timely and accurately. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Fault Diagnosis Method for Lithium Batteries Based on Optimal Variational Modal Decomposition and Dimensionless Feature Parameters | |
type | Journal Paper | |
journal volume | 20 | |
journal issue | 3 | |
journal title | Journal of Electrochemical Energy Conversion and Storage | |
identifier doi | 10.1115/1.4055536 | |
journal fristpage | 31004 | |
journal lastpage | 3100410 | |
page | 10 | |
tree | Journal of Electrochemical Energy Conversion and Storage:;2022:;volume( 020 ):;issue: 003 | |
contenttype | Fulltext | |