The design and modeling of an interior permanent magnet synchronous motor’s fuzzy sliding mode speed control for a three-wheeler electric bajaj are examined in this thesis. Due to its excellent efficiency, small volume, light weight, high reliability, maintenance-free nature, adequate control features, and capacity to draw attention from the EV industry, interior permanent magnet synchronous motor are highly preferred in electric vehicle technology.Because of the nonlinear dynamic model of the interior permanent magnet synchronous motor, sliding mode control was first employed. However, this led to chattering in the control input response, Therefore, a fuzzy sliding mode controller was created to lessen chattering and enhance the dynamics tracking performance of the interior permanent magnet synchronous motor system’s speed regulation. Additionally, the Genetic Algorithm was used to automatically optimise the SMC parameters in order to find the best values.Using a field-oriented control technique, the control concept and the implementation of space vector pulse width modulation are examined and in order to completely utilize the reluctance torque and increase the motor’s starting speed, Maximum Torque Per Ampre regulation was employed.MATLAB/Simulink software was used to compare the suggested controller to alternative controllers (such as GA-SMC and traditional SMC). The simulation’s findings showed that the system’s intended Fuzzy sliding mode controller realized a good dynamic behavior. Its perfect speed tracking and control coefficients ensured robustness against sudden load disturbances and parameter variations, while the system maintained a good dynamic performance with rise and settle times of 0.0096 and 0.0142 seconds, 0.0888% overshoot, -0.00102 steady state error, 0.00596 ITAE and it eliminates the chattering effect and give better performance compared to GA-SMC and conventional SMC. Keywords: Interior Permanent Magnet Synchronous Motor, Electric Vehicle, Fuzzy Sliding Mode Control, Field Oriented Control,Genetic Algorithm,Maximum Torque Per Ampre.