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In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment.

In this paper we propose a real-time nonlinear Model Predictive Control strategy for stabilisation of a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear four-wheel vehicle model coupled with a nonlinear tyre model are used to design three Model Predictive Control strategies of different levels of complexity that are implementable online: one that uses a linearized version of the vehicle model and then solves the resulting Quadratic Program problem, a second one that employs the Real Time Iteration scheme on the nonlinear Model Predictive Control problem and a third one that applies the Primal Dual Interior Point method on the nonlinear Model Predictive Control problem instead until convergence. After analysing the relative trade-offs in performance and computational cost between the three Model Predictive Control strategies by comparing them against the optimal solution in a series of simulation studies, we test the most promising solution in a high fidelity environment.

Modern Hybrid Electric Vehicles employ electric braking to recuperate energy during deceleration. However, currently Anti-lock Braking System (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.

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