Abstract
Magnetless low-cost switched reluctance motor (SRM) speed-torque curve ideally suited for low-power electric vehicles (EVs), The few issues with adopting SRM to EVs are commutation torque and flux ripple. Researchers are changing SRM design topologies in rotor structure and many control techniques to minimize the SRM drawbacks. Therefore, this paper proposed a sensorless field-oriented control (SLFOC) based adaptive neuro-fuzzy inference system (ANFIS) approach with four phases 8/6 SRM at a rated power of 3kW control speed, torque response behavior, and mitigate torque ripple. This model was investigated and validated using MATLAB/Simulink®. Thus, it improves the dynamic response for SRM-EVs. Additionally, performance comparisons on fuzzy logic, neural networks, and proposed SLFOC-ANFIS are presented. The simulation results are validated the speed estimation, torque, and flux ripple.
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