Fault Diagnosis in Electrical Machines for Traction Applications: Current Trends and Challenges

Marco Pastura and Mauro Zigliotto ()
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Marco Pastura: Department of Engineering and Management (DTG), University of Padua, I-36100 Vicenza, Italy
Mauro Zigliotto: Department of Engineering and Management (DTG), University of Padua, I-36100 Vicenza, Italy

Energies, 2024, vol. 17, issue 21, 1-20

Abstract: The widespread diffusion of electric vehicles poses new challenges in the field of fault diagnostics. Past studies have been focused mainly on machines designed for industrial applications, where the operating conditions and requirements are significantly different. This work presents a review of the most recent studies about fault diagnosis techniques in electrical machines feasible for traction applications, with a focus on the most adopted approaches of the last years and on the latest trends. Considerations about their applicability for electric vehicle purposes, along with some areas that require further research, are also provided.

Keywords: fault detection; review; electric vehicle; traction application; condition monitoring; machine learning; permanent magnet machines; induction machines; multi-phase machines (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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