Optimal torque vectoring control strategies for stabilisation of electric vehicles at the limits of handling

Abstract

The study of chassis control has been a major research area in the automotive industry and academia for more than fifty years now. Among the popular methods used to actively control the dynamics of a vehicle, torque vectoring, the method of controlling both the direction and the magnitude of the torque on the wheels, is of particular interest. Such a method can alter the vehicle’s behaviour in a positive way under both sub-limit and limit handling conditions and has become even more relevant in the case of an electric vehicle equipped with multiple electric motors. Torque vectoring has been so far employed mainly in lateral vehicle dynamics control applications, with the longitudinal dynamics of the vehicle remaining under the full authority of the driver. Nevertheless, it has been also recognised that active control of the longitudinal dynamics of the vehicle can improve vehicle stability in limit handling situations. A characteristic example of this is the case where the driver misjudges the entry speed into a corner and the vehicle starts to deviate from its path, a situation commonly referred to as a ‘terminal understeer’ condition. Use of combined longitudinal and lateral control in such scenarios have been already proposed in the literature, but these solutions are mainly based on heuristic approaches that also neglect the strong coupling of longitudinal and lateral dynamics in limit handling situations. The main aim of this project is to develop a real-time implementable multivariable control strategy to stabilise the vehicle at the limits of handling in an optimal way using torque vectoring via the two independently controlled electric motors on the rear axle of an electric vehicle. To this end, after reviewing the most important contributions in the control of lateral and/or longitudinal vehicle dynamics with a particular focus on the limit handling solutions, a realistic vehicle reference behaviour near the limit of lateral acceleration is derived. An unconstrained optimal control strategy is then developed for terminal understeer mitigation. The importance of constraining both the vehicle state and the control inputs when the vehicle operates at the limits of handling is shown by developing a constrained linear optimal control framework, while the effect of using a constrained nonlinear optimal control framework instead is subsequently examined next. Finally an optimal estimation strategy for providing the necessary vehicle state information to the proposed optimal control strategies is constructed, assuming that only common vehicle sensors are available. All the developed optimal control strategies are assessed not only in terms of performance but also execution time, so to make sure they are implementable in real time on a typical Electronic Control Unit.