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Torque Ripples Minimization of DTC IPMSM Drive for the EV Propulsion System using a Neural Network  

Singh, Bhim (Department of Electrical Engineering, Indian Institute of Technology)
Jain, Pradeep (Department of Instrumentation and Control Engineering, NSIT)
Mittal, A.P. (Department of Instrumentation and Control Engineering, NSIT)
Gupta, J.R.P. (Department of Instrumentation and Control Engineering, NSIT)
Publication Information
Journal of Power Electronics / v.8, no.1, 2008 , pp. 23-34 More about this Journal
Abstract
This paper deals with a Direct Torque Control (DTC) of an Interior Permanent Magnet Synchronous Motor (IPMSM) for the Electric Vehicle (EV) propulsion system using a Neural Network (NN). The Conventional DTC with optimized switching lookup table and three level torque controller generates relatively large torque ripples in an electric vehicle motor drive. For reducing the torque ripples, a three level torque controller is hereby replaced by the five level torque controller. Furthermore, the switching lookup table of the five level torque controller based DTC is replaced with a Neural Network. These DTC schemes of an IPMSM drive are simulated using MATLAB/SIMULINK. The simulated results are compared with the conventional DTC and it is found that the ripples in the torque, as well as in the stator current, are reduced drastically.
Keywords
DTC; Electric vehicle; IPMSM; Neural network;
Citations & Related Records

Times Cited By Web Of Science : 2  (Related Records In Web of Science)
Times Cited By SCOPUS : 3
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