Intelligent Speed Regulation Control in Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

Abstract

This paper studies the intelligent speed regulation control of switched reluctance motor of electric vehicle based on neural network parameter identification. Starting with the analysis of the performance of switched reluctance motor, the nonlinear flux linkage characteristic inversion model and torque characteristic model of switched reluctance motor are established based on BP neural network. This paper studies and improves the fast self configuration algorithm of BP neural network. Finally, the nonlinear simulation model of switched reluctance motor is established under Matlab/Simulink. The model can be used for further control research. In this paper, the integrated control method of instantaneous torque control based on torque observation and three-step commutation control is studied, and the simulation analysis is carried out. The results show that this method can effectively reduce the torque ripple of switched reluctance motor and improve the performance of its drive system.