Real time condition monitoring and fault diagnosis of propulsion motors used in electric vehicles

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The proposed research program aims to accomplish real-time condition monitoring and fault diagnosis of propulsion motors used in electric vehicles (EV). Propulsion motor is a critical component of electric vehicle be it ground electric vehicle such as electric car, electric bus etc. or seaborne electric vehicle such as electric boat, ferry etc. or air borne electric vehicle such as manned and unmanned aerial vehicle. Any unexpected failure of propulsion motor in an EV would affect the users as well as the manufacturers of the EV. For the users, an unexpected failure of propulsion motor could be as small as the inconvenience of EV not working or could be as big as a catastrophic accident. For an EV manufacturer, frequent and abrupt failure of propulsion motors would severely dent one's brand image and hence drive away prospective customers. Furthermore, repair and replacement costs might be quite high. Therefore, it is vital to have a technology that would monitor the condition of the propulsion motor in real-time and warn about faults at an incipient stage. There are many different types of faults that can occur in a propulsion motor. Moreover, each fault propagates at different rates and afflicts the performance of a propulsion motor to different extent. Thus, the fault detection system should be able to segregate the fault type and estimate the severity of each fault. Furthermore, propulsion motor has numerous designs based on power rating, performance, space, cost, operating conditions etc. of an electric transportation system. Consequently, the condition monitoring and fault diagnostic scheme should be able to accommodate these design variations without compromising on reliability. The proposed research program intends to develop such a comprehensive condition monitoring and fault diagnostic scheme that would cater to the full spectrum of electric transportation systems. The research would adopt a mathematical model based approach (a) to study the impact of different types and severities of internal faults on the performance of propulsion motors, (b) to analyze the impact of variations in propulsion motor design parameters like air-gap length, rotor type, slotting and winding arrangement etc. on fault identification and segregation schemes, and (c) to train and develop an artificial intelligence based real-time condition monitoring and fault detection system. The research program's outcomes will have a significant bearing on accelerating electric transportation systems to market. Additionally, the research program will generate highly skilled HQPs to meet the demands of electric transportation sector. Finally, the proposal will allow a world class program in condition monitoring and fault diagnosis of propulsion motors to be developed in Canada at the University of Victoria that are currently lacking in other Canadian universities.