Enhancing Electric Vehicle Performance of Electrical Motors and Control Methods Using Machine Learning Techniques

The growing adoption of electric vehicles (EVs) necessitates advancements in motor performance to overcome range limitations caused by the lower energy density of batteries compared to fossil fuels. This study aims to conduct a comparative analysis of electric motors for EV applications, focusing on Permanent Magnet Synchronous Motors (PMSMs). Techniques like Non-Destructive Testing (NDT) with magnetic flux density mapping and Direct Torque Control (DTC) were employed to enhance dynamic response. The study also explored model predictive control (MPC) to optimize efficiency and reduce energy losses. Results revealed that the "Speed Master Elite" motor achieved an outstanding 92% efficiency, showcasing its potential for superior power output. Future work includes developing smart charging infrastructure to optimize EV charging based on grid and energy availability, further enhancing EV performance and sustainability.