Model Predictive Torque Vectoring Control for a Formula Student Electric Racing Car

In this paper we present two torque vectoring control algorithms for an electric racing car with independent all-wheel drive. A nonlinear, two-track vehicle model is used for the design of a linear time-varying model predictive controller and a nonlinear model predictive controller, while the unknown system states are estimated by the unscented Kalman filter. The controller has been tested in various simulation scenarios and the obtained results are compared with the case assuming equal torque distribution, i.e., without torque vectoring. The results show that the proposed optimization-based torque vectoring control strategy may effectively stabilize the vehicle at the limits of handling and in this way increase its track performance.