Neural Network based Direct Torque Controller of SRM for EV Application

Ganesh D, Professor, Department of Computer Science and Information Technology, Jain (Deemed to be University), Email Id- d.ganesh@jainuniversity.ac.in, Bangalore, India
Vijay Saxena, Admission Head, Department of Management, Sanskriti University, Email Id-admissions@sanskriti.edu.in, Mathura, Uttar Pradesh, India
Daljeet Pal Singh, Assistant Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Email Iddaljeetpalsingh1768@gmail.com, Uttar Pradesh, India
Pramod Kumar Faujdar, Associate professor, Mechanical Engineering, Vivekananda Global University, Email Id-pramod_kumar@vgu.ac.in, Jaipur, India

^* corresponding Author: d.ganesh@jainuniversity.ac.in

Abstract

the control aspects of the elecytric vehicle are presented in this paper. The electric car which is driven by a 6/4 Switched Reluctance Motor (SRM) powered by four battery banks is presented in this paper. The new topology of converter is proposed to drive SRM effectively for application of electric car. The direct torque controller is implemented with the help of neural network for effective speed controller with minimum ripples in torque. The required pulses are generated with space vector Pulse Width Modulation (PWM) technique. The electromagnetic torque generated by SRM needs to be maintained at ripples free for smooth operation of electric car. The mathematical validation is implemented to achieve the required power rating of SRM for Toyota Car. The proposed topology of converter has a facility of using four battery banks; hence the charging time of batteries will be minimized. The proposed model is designed on the platform of the MATLAB/Simulink package which is dumped into OPAL-RT modules to establish Hardware – in the – Loop (HIL) for presentation of various results. Various results are discussed with validate explanations of the proposed method.