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About the Project

Fault Diagnosis of Electrical Motors in Electric Vehicles based on Artificial Intelligence Approach
Electrical motors form an integral part of any Electric Vehicle (EV). Most EV manufacturers currently prefer induction motors (IM) or permanent magnet synchronous motors (PMSM) as propulsion motors because the former offers low cost, ruggedness, and less maintenance. The latter offers high power density with high efficiency to permanent magnets. Any problem with the motor’s operation will have an impact on the entire EV. As a result, detecting faults at an early stage will allow us to avoid catastrophic damage to the propulsion motor, take appropriate preventive action, and reduce financial losses. Bearing and eccentricity (69%) are the most common faults in propulsion motors (IM or PMSM), followed by stator winding (21%), rotor (7%), and other faults (3%). Since propulsion motor covers a significant portion of total EV cost, it is essential to diagnose the faults at the incipient stage to prevent catastrophic damage and reduce financial losses. For the development of fault detection frameworks, cutting-edge signal processing features will be analysed in conjunction with artificial intelligence-based algorithms.

Department and Campus

Department of Electrical and Electronics Engineering, School of Engineering, Coimbatore

Skillsets Preferred from Applicants

Above 80% in M.Sc., chemistry


Electric Vehicles (Fault Diagnosis of Electrical Motors)
Praveenkumar N.

Assistant Professor (Sr. Gr.)
Department Of Electrical and Electronics Engineering
School of Engineering, Coimbatore

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