This dissertation presents the development and optimization of a torque vectoring system for a Formula Student vehicle, aimed at enhancing dynamic stability and handling performance. The research addresses four key objectives: creating a realistic vehicle model, identifying the optimal control strategy, ensuring a high level of safety, and enabling future improvements for the vehicle and the team. Simulink, in conjunction with data from VSM laptime simulation software, was used to model the vehicle and integrate three different torque distribution controllers: a proportional-integral-derivative (PID) controller, a sliding mode controller, and a fuzzy logic controller. The performance of these controllers was evaluated by comparing the vehicle’s yaw acceleration to an ideal reference value. Among the tested controllers, the fuzzy logic controller demonstrated the best performance in terms of response speed, accuracy, and reduced oscillations. To ensure safety, a failsafe mechanism and dashboard alerts were implemented, mitigating risks associated with torque vectoring malfunctions. This study provides a foundation for improving vehicle dynamics in Formula Student competition cars and offers insights into future developments for the team. Highlights • Development and optimization of a torque vectoring system to significantly enhance dynamic stability and handling performance for a Formula Student vehicle. • Test and comparison of three torque distribution controllers—PID, sliding mode, and fuzzy logic—identifying the fuzzy logic controller as the best performer in response speed, accuracy, and reduced oscillations. • Implementation of a failsafe mechanism and dashboard alerts to ensure high safety levels, protecting against potential torque vectoring malfunctions. • Foundation for improving vehicle dynamics and offering valuable insights for future advancements in Formula Student competition cars.