An Adaptive Fault Diagnosis of Electric Vehicles: An Artificial Intelligence Blended Signal Processing Methodology-Reference-Cited by-同舟云学术

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An Adaptive Fault Diagnosis of Electric Vehicles: An Artificial Intelligence Blended Signal Processing Methodology

Published:2023 Issue:3 Volume:46 Page:196-206
ISSN:2694-1783
Container-title:IEEE Canadian Journal of Electrical and Computer Engineering
language:
Short-container-title:IEEE Can. J. Electr. Comput. Eng.

Author:

Gong Lingli¹,Sharma Anshuman¹^ORCID,Bhuiya Mohammad Abdul¹^ORCID,Awad Hilmy²^ORCID,Youssef Mohamed Z.¹^ORCID

Affiliation:

1. Department of Electrical, Computer and Software Engineering, School of Engineering and Applied Science, Power Electronics and Drives Applications Laboratory (PEDAL), Ontario Tech University, Oshawa, ON, Canada

2. Department of Electrical and Computer Engineering, Helwan University, Helwan, Egypt

Funder

NSERC CANADA Collaborative Research and Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

http://xplorestaging.ieee.org/ielx7/9349829/10175198/10155401.pdf?arnumber=10155401

Reference24 articles.

1. Ground Fault Location System for Powertrain of Electric Vehicles

2. Thermographic Fault Diagnosis of Shaft of BLDC Motor

3. A Novel Detection Method for Open-Circuit Fault in Modular Multilevel Converters

4. Thermographic fault diagnosis of electrical faults of commutator and induction motors

5. Artificial Neural Network Based Open-Transistor Fault Detection in Symmetric Cascaded H-Bridge Multilevel Inverter

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