Vehicle dynamic control is crucial for ensuring safety, efficiency and high performance. In formula-type electric vehicles equipped with in-wheel motors (4WD), traction control combined with torque vectoring enhances stability and optimizes overall performance. Precise regulation of the torque applied to each wheel minimizes energy losses caused by excessive slipping or grip loss, improving both energy efficiency and component durability. Effective traction control is particularly essential in high-performance applications, where maintaining optimal tire grip is critical for achieving maximum acceleration, braking, and cornering capabilities. This study evaluates the benefits of Fuzzy Logic-based traction control and torque distribution for each motor. The traction control system continuously monitors wheel slip, ensuring they operate within the optimal slip range. Then, torque is distributed to each motor according to its angular speed, maximizing vehicle efficiency and performance. Thus, a longitudinal dynamic model was implemented in MATLAB/Simulink, incorporating traction forces, rolling resistance, aerodynamic drag and downforce, and load transfer during acceleration and braking. Tire grip was also modeled using the Pacejka formula, with data from the Tire Test Consortium (TTC). As a result, the model allows the calculation of acceleration, velocity, position, and the vehicle’s slip ratio. To simulate vehicle dynamic behavior, a representative driving cycle was defined and associated with an auxiliary control that emulates the driver throttle and braking inputs, aiming to match the desired speed profile. This approach allows the development and calibration of the fuzzy logic traction control, optimizing the vehicle performance.