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

Prashant Shivajirao  Mali; D. Susitra

doi:10.6180/jase.202511_28(11).0015

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

Prashant Shivajirao Mali^1,2This email address is being protected from spambots. You need JavaScript enabled to view it. and D. Susitra¹

¹Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India

²Department of Electrical Engineering, Annasaheb Dange College of Engineering and Technology, Ashta, India

Received: July 5, 2024
Accepted: January 21, 2025
Publication Date: March 16, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202511_28(11).0015

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.

Keywords: Electric Vehicle; Non-destructive Testing; Direct Torque Control; Permanent Magnet Synchronous Motor; Model Predictive Control; Electrical Motors

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