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Abstract

The popularly used Switched Reluctance Motor (SRM) is an electric motor that employs reluctance torque and includes salient poles on the stator and rotor. A Modified Crow Search Algorithm (MCSA) based on an asymmetric converter with a PI-PWM controller is used in this paper to control the speed of a Switched Reluctance Motor (SRM) while also minimizing torque ripple. To control the settling time, the controller takes the optimum value of the torque ripple & the square root of the speed error. The motor works simultaneously with two phases during normal operation, but it is designed to satisfy the load specifications of the faulty single-phase & two-phases. A Modified Crow Search Algorithm has been used successfully in the improvement of parameters that controls the speed of SRM. Settling time, Rise time, Steady-state error, Peak Overshoot & Torque ripple parameters can be reduced in this presented method. Optimization technique is implemented to adjust the PI controller gain parameters in order of improving torque and speed control. The efficiency of the proposed optimization algorithm based on speed, current, torque, and flux is shown through simulation results. The motors that can be utilised in EVs include switching reluctance motors, permanent magnet synchronous motors, induction motors, brushless DC motors, and brushed DC motors. The SRM has established as one of the best solutions for electric vehicle propulsion because to its clear benefits over other types of electric drive systems. This paper will model the SRM drive for EV applications using MATLAB/Simulink.

Keywords

switched reluctance motor (SRM)proportional integral (PI)pulse width modulation (PWM)modified crow search algorithm (MCSA)electric vehicle (EV)

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A Modified Crow Search Algorithm is used to Minimize Torque Ripple for Switched Reluctance Motor Drives in Electric Vehicle Application

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