Torque Vectoring for a Hybrid Formula Student Vehicle

The University of Padova Formula Student team has recently transitioned from a conventional combustion-powered car to a hybrid powertrain configuration, featuring two front electric motors and a rear internal combustion engine. The objective of this thesis is the development of two different Torque Vectoring algorithms for the Formula Student vehicle, aiming to take full advantage of its new configuration. The first aims to maximize the possible lateral force of the front axle, thereby improving cornering capability, while the second is designed to reduce understeer and oversteer tendencies, improving driver confidence. Both control strategies have been modeled in MATLAB/Simulink and simulated in VI-CarRealTime. The results provide insight into the benefits and limitations of each approach and establish a foundation for future work towards an optimal trade-off between the two.