Characterization of Onboard Condition Monitoring Techniques for Stator Insulation Systems in Electric Vehicles: A Review

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

The design of the stator insulation system for a traction machine used in electric vehicles is a challenge due to the tight requirements set on compact size, high dynamic stresses and a lack of automotive specific insulation standards. A poorly designed stator insulation can lead to safety issues and stator faults. Approximately, 20-30% of faults in traction machines are stator faults. In order to safeguard a healthy operation of the traction machine, condition monitoring of the traction machine while the electric vehicle is running or at standstill can be applied. This paper summarizes the state of the art of stator insulation systems, presents various root causes leading to stator faults and classifies onboard condition monitoring techniques under a proposed characterization structure, distinguishing between self analysis and traction mode. This characterization aims at providing a broad perspective on stator fault monitoring techniques to automotive engineers and researchers, focusing on permanent magnet synchronous machines.

Original languageEnglish
Title of host publicationIECON 2019
Subtitle of host publication45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages3179-3186
Number of pages8
ISBN (Electronic)9781728148786
ISBN (Print)978-1-7281-4879-3
DOIs
Publication statusPublished - 2019 Dec 9
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 2019 Oct 142019 Oct 17

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
PublisherIEEE
Volume2019
ISSN (Print)1553-572X
ISSN (Electronic)2577-1647

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/TerritoryPortugal
CityLisbon
Period2019/10/142019/10/17

Subject classification (UKÄ)

  • Embedded Systems

Keywords

  • automotive
  • condition monitoring
  • diagnostics
  • EV
  • stator fault
  • stator insulation system
  • traction machine

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