Reduction of torque ripple in induction motor-driven electric vehicle using optimized stator flux

• Published in: International journal of information technology (Singapore. Online) Vol. 15; no. 3; pp. 1333 - 1346
• Main Authors: Sahoo, Anjan Kumar, Jena, Ranjan Kumar
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.03.2023
• Subjects: Artificial Intelligence; Computer Imaging; Computer Science; Image Processing and Computer Vision; Machine Learning; Original Article; Pattern Recognition and Graphics; Software Engineering; Vision; Fuzzy logic control; Electric vehicle; Induction motor drive; Torque ripple; Direct torque control
• ISSN: 2511-2104; 2511-2112
• DOI: 10.1007/s41870-023-01172-3

This paper presents a novel reference flux selection technique for reducing torque ripple in an induction motor-based direct torque control (DTC) strategy for electric vehicle (EV) applications. Due to its fast torque response and simplicity, DTC is more popular for EVs. However, a hysteresis controller and limited voltage vectors in DTC result in a variable switching frequency, which creates torque and flux ripples. These ripples can be mitigated by choosing an appropriate reference flux according to the driving scenario instead of a fixed reference. The torque ripple as a function of flux and its optimal value for which the ripple will be the minimum are established numerically here. The effectiveness of the proposed approach over conventional DTC and fuzzy DTC is verified using a 50-hp induction motor (IM) drive in simulation using MATLAB in different operating conditions. As performance indicators, torque and flux ripple, current harmonics, and integral square error are evaluated.