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This paper describes a model predictive control based rear wheel torque vectoring algorithm of a 4WD vehicle for improving the driving stability. The model predictive control algorithm has been used to compute the optimal longitudinal tire forces of rear-left and rear-right wheels based on the linearized error dynamics. The used error dynamic model has been derived from the planar vehicle dynamic model that was represented by longitudinal, lateral, and yaw dynamics. In order to compute the reasonable optimal tire forces, dynamic physical constraints(e.g., tire force limit and change rate limit of tire force) have been applied to the model predictive control algorithm. Based on the computed optimal tire forces of the rear wheels, the torque distribution ratio between the rear-left wheel and rear-right wheel of the vehicle has been computed based on the wheel dynamics. The yaw rate in a steady state has been used as a reference value for improving the driving stability. The use of the proposed model predictive torque vectoring algorithm can bring about not only good torque distribution, but also sound driving stability. For the performance evaluation, simulation studies with the proposed torque vectoring algorithm have been conducted on Matlab/CarSim environment under various driving conditions, such as step steer, double lane change, and high speed avoidance. The simulation results showed that the proposed model predictive torque vectoring algorithm produced both reasonable torque distribution and good driving stability.

Model-predictive-control based Rear Wheel Torque Vectoring Algorithm for 4WD Vehicle to Improve Driving Stability