Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

Designing of Next Generation Motor Drive Control for Electric Vehicle Application

In order to achieve a mission of zero emission, as most automotive industries around the world are pledging to, the research and production of efficient and eco-friendly electrified vehicles (EVs) is a necessary goal to pursue. They are of high interest to governments and research facilities across the world as they have higher efficiency levels and are more environmentally friendly than current gasoline vehicles. At the core of electric vehicle application, electric motor drives act an important role to direct the motor to convert electrical energy into mechanical energy and provide electrical control of the processes. Therefore, it is required for researchers to make the motor drive more energy-efficient and have bi-directional power flow capability to ensure the improvement of motor performance and be flexible regarding controllability. The goal of the author is to investigate the development of a better motor-drive to achieve a control that provides a superior control of the traction motor. This requires improving the existing flux weakening motor control that is used for traction application. The improved control is programmed and hard coded into a Digital signal processor which is embedded in the control drive board. In a conventional inverter, this drive unit controls the gate drivers which in turn controls the IGBTs, there by enabling variation in operating performance of the motor. Currently, there is a lack of unified program that can operate any kind of traction motor like permanent magnet synchronous motors (PMSM) or induction motors(IM). This is leading automotive industries to invest a lot of resources in research and development in this field of work so that the future vehicles can be swapped with any motor as per requirement. The authors are currently working on developing this motor control and also reducing the complexity of the code and real-time operation on the microcontroller. This will be implemented in future on existing and new-generation inverters to test the control on various motor and inverter setups