Sensor-less DTC Control of SRM for EV Using Artificial Intelligence

A Switched Reluctance Motor (SRM) is a robust electrical motor with characteristics that make it suitable for use in aerospace and electric vehicle applications because of its straightforward design. SRM drives have grown in popularity in electric vehicle applications due to its excellent efficiency, dependability, sturdy construction, and minimal rotor losses. However, this type of motor’s main shortcomings for use in electric car applications are current and torque ripples and External rotor position sensor required. Various control strategies are created utilizing simulation software to lessen the ripple contents for adjusting the speed of the SRM drive. In order to emphasize the performance of an efficient controller, this work aims to examine a technique to dispense with the speed sensor by applying the speed estimator and focuses on the comparison of the fuzzy logic with Adaptive-Neuro Fuzzy Inference Strategy (ANFIS) When DTC technology is applied to an SRM drive, The results are different between these techniques and each technique performs differently with DTC. Simulink environment is used for simulation tasks.