In this paper, the problem of stability control of an electric vehicle is addressed. To this aim, it is required that the vehicle follows a desired yaw rate at all driving/road conditions. The desired yaw rate is calculated based on steering angle, vehicle speed, vehicle geometric properties as well as road conditions. The vehicle response is modified by torque vectoring on front and/or rear axles.This control problem is subject to several constraints. The electric motors can only deliver a certain amount of torque at a given rotational speed. In addition, the tire capacity also plays an important role. It limits the amount of torque they can transfer without causing wheel to slip excessively. These constraints make the Model Predictive Control (MPC) approach a suitable choice, because it can explicitly consider the constraints of the control problem, in particular the tire capacity constraint, and help prevent tire saturation, which is often the cause of vehicle instability.Successful implementation of a control system often requires filtering the noise in measured signals. Such filters attenuate the high frequency noise in the sensor output, but they also introduce a certain amount of phase shift (time lag) in the signal. Depending on filter specifications (filter type, order and noise attenuation level), the amount of delay varies. The delays in the vehicle stability control system may be inconsequential in normal driving conditions, but can degrade the performance of most controllers in critical driving conditions that involve high speed cornering or adverse road conditions. Besides, filters are not the only source of delay in a control system. Actuators often exhibit a considerable amount of delay in delivering the requested torque.In this paper, a method is developed that allows dealing with delays from different sources in the framework of model predictive control. To this end, the prediction model is used to predict the system states at the end of the delay period. This is the earliest time that the effect of any control action is visible on the system. Afterwards, the control action is determined based on this new state. The proposed method is easy to implement and unlike robust control techniques, does not entail a complicated tuning process.The performance of the model predictive controller with the proposed delay-handling method is investigated using computer simulations with a high-fidelity CarSim model of a four wheel drive electric SUV. Comparison is made with the same controller when the system delay is ignored in the controller design. It can be seen that the new controller shows a superior performance in severe driving conditions and can better prevent vehicle instability.