Mengesha Mamo (PhD)Biruk Abebe2025-10-072025-10-072025-06https://etd.aau.edu.et/handle/123456789/7471Nowadays, the popularity and demand for electric vehicles have increased, and much effort has been devoted to the creation of high-performance EV drives. This is primarily to reduce environmental pollution caused by emissions from internal combustion engine (ICE)-powered vehicles, and to replace fossil fuels with renewable energy sources because of their rapid depletion. Electric motors are the key components of electric vehicles; thus, their selection is important due to their effects on the performance of the entire system. Many types of electric motors have been analyzed and evaluated for the use electric vehicles. Switched Reluctance Motor (SRM) are suitable for high-speed drive application due to the absence permanent magnet and winding in the rotor. SRM have a numerous of advantages over other electric motors due to their robust structure, low cost, ability of fault-tolerant and resilience. In spite of these advantages, SRM suffers from high torque ripple which will result in undesired vibration and acoustic noise. When taking into account the needs of traction application, the most significant and difficult SRM concerns are torque ripple minimization and reference speed tracing. An adequate control scheme is essential for the drive to have a good dynamic and transient response and also to tracks the reference speed while minimizing the ripple torque of the SRM. In this thesis, a fuzzy logic controller is designed to increase system performance by reducing torque ripple and eliminating the speed reference tracking problem. The designed controller enables the actual speed to follow closely with the reference speed in 0.047 seconds. It shows 59.04% and 53.47% improvement in rise time compared to the PI controller and conventional FLC, respectively. Simulation results were obtained using the MATLAB/Simulink environment for the effectiveness of the study.en-USElectric vehicleSwitched reluctance motorDirect torque controlFuzzy logic controlMATLAB/SimulinkThesis