Failure Mode Mechanism and Effect Analysis of High Voltage DC Arcs in Electric Vehicle Cable

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With a global increase in electric vehicles (EV) the reliability, quality and performance of EVs is becoming increasingly important. High voltage direct current (HVDC) arc events in cables can lead to fires within EVs. Therefore, in this paper we investigate the failure modes mechanisms and effect analysis (FMMEA) of this safety critical issue. The onset of these failure modes can be multi-factorial so we created a statistical design of experiment to investigate the evolution and mitigation of these failures. Analysis of the arc characteristics collected in laboratory models has been decoupled and classified into time sub-domains. Utilising continuous wavelet transform (CWT) for capturing frequency features and minimizing the influence or erroneous signals. Verification testing was performed using a reference arc model in Simulink for comparative analysis of the experimental and simulated data. Mitigation of arcs via the incorporation of a fuse has been proven to provide sufficient circuit protection. Our results indicate that our methodology provides an accurate framework for cable arc classification and mitigation.
Original languageEnglish
Title of host publication2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)
EditorsTek-Tjing Lie, Youbo Liu
PublisherIEEE
Pages1316-1321
Number of pages6
ISBN (Electronic)9781728191591
DOIs
Publication statusPublished - 25 May 2021
Event6th Asia Conference on Power and Electrical Engineering 2021 - Chongqing, China
Duration: 8 Apr 202111 Apr 2021
https://www.aconf.org/conf_177275.html

Conference

Conference6th Asia Conference on Power and Electrical Engineering 2021
Abbreviated titleACPEE 2021
CountryChina
CityChongqing
Period8/04/2111/04/21
Internet address

Keywords

  • Data Analysis
  • Electric Vehicles
  • FM-MEA
  • HVDC
  • Reliability

ASJC Scopus subject areas

  • Artificial Intelligence
  • Management of Technology and Innovation
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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