A fault diagnosis method for lithium batteries based on optimal variational modal decomposition and dimensionless feature parameters

IF 2.7 4区工程技术 Q3 ELECTROCHEMISTRY

Journal of Electrochemical Energy Conversion and Storage Pub Date : 2022-09-07 DOI:10.1115/1.4055536

C. Chang, Chengcheng Tao, Shaojin Wang, Ruhang Zhang, Aina Tian, Jiuchun Jiang

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引用次数: 0

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 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 firstly preprocesses the voltage signal of lithium battery by optimal variable mode decomposition to obtain the high and low frequency components of the signal, and reconstructs the high and low frequency 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 local 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 vehicles thermal runaway failure, this method can detect the faulty battery timely and accurately.

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基于最优变分模态分解和无量纲特征参数的锂电池故障诊断方法

由于电池系统故障引起的电动汽车安全事故频繁发生，为了保证车辆的正常运行，对电动汽车锂电池进行故障诊断至关重要。提出了一种基于最优变分模态分解和无量纲特征参数的锂电池故障诊断方法。该方法首先通过最优变模分解对锂电池电压信号进行预处理，得到信号的高低频分量，并对高低频分量进行重构。然后根据重构信号提取无量纲特征参数，利用局部线性嵌入算法对无量纲特征参数进行特征约简。最后，采用局部离群因子算法对故障电池进行检测。通过对真实电动汽车热失控故障发生前运行数据的验证，该方法能够及时、准确地检测出故障电池。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Electrochemical Energy Conversion and Storage Engineering-Mechanics of Materials

CiteScore

4.90

自引率

4.00%

发文量

期刊介绍： The Journal of Electrochemical Energy Conversion and Storage focuses on processes, components, devices and systems that store and convert electrical and chemical energy. This journal publishes peer-reviewed archival scholarly articles, research papers, technical briefs, review articles, perspective articles, and special volumes. Specific areas of interest include electrochemical engineering, electrocatalysis, novel materials, analysis and design of components, devices, and systems, balance of plant, novel numerical and analytical simulations, advanced materials characterization, innovative material synthesis and manufacturing methods, thermal management, reliability, durability, and damage tolerance.