Neuro-fuzzy based estimation of rotor flux for Electric Vehicle operating under partial loading

• Published in: Journal of intelligent & fuzzy systems 2021-01, Vol.41 (5), p.5653-5663
• Main Authors: Kumar, Manish, Kumar, Bhavnesh, Rani, Asha
• Format: Article
• Language: English
• Subjects: Adaptive systems; Artificial neural networks; Electric vehicles; Fuzzy logic; Induction motors; Inference; Load fluctuation; Mathematical models; Model accuracy; Motor rotors; Neural networks; Parameter sensitivity
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-189885

The primary objective of this work is to optimize the induction motor rotor flux so that maximum efficiency is attained in the facets of parameter and load variations. The conventional approaches based on loss model are sensitive to modelling accuracy and parameter variations. The problem is further aggravated due to nonlinear motor parameters in different speed regions. Therefore, this work introduces an adaptive neuro-fuzzy inference system-based rotor flux estimator for electric vehicle. The proposed estimator is an amalgamation of fuzzy inference system and artificial neural network, in which fuzzy inference system is designed using artificial neural network. The training data for neuro-fuzzy estimator is generated offline by acquiring rotor flux for different values of torque. The conventional fuzzy logic and differential calculation methods are also developed for comparative analysis. The efficacy of developed system is established by analyzing it under varying load conditions. It is revealed from the results that suggested methodology provides an improved efficiency i.e. 94.51% in comparison to 82.68% for constant flux operation.