A modular Torque Vectoring System for a 4WD Electric Performance Vehicle

This master’s thesis proposes a modular torque vectoring system for an electric performance vehicle with four independent motor-generator units (MGU). The implemented system is subdivided into three subsystems, consisting of a vehicle motion reference formulation, a chassis force control algorithm and a control allocation algorithm, where the former two belong the outer-loop and the latter belongs to the inner-loop of a cascaded control structure. The vehicle motion reference is based on three derivations of the states governing a single track model and its influence is studied on stability critical maneuvers. Gain scheduled PI control is used for yaw moment control, where the computational performance gain of a dynamic saturation is evaluated. A non-linear constrained optimization problem is solved in the control allocation subsystem, where the influence of weighing factors in the cost function is analyzed. The whole system performance is simulated in a software-in-the-loop (SIL) environment for different maneuvers, varying the control configurations and respective parameters