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Electric vehicles (EVs) have been gaining a lot of focus and attention as they run clean and are environment friendly. EVs use in-hub motors, which can be independently controlled, improving this way the maneuverability and allowing augmented control actions. This paper discusses the development of a Model Predictive Controller (MPC) to improve vehicle handling characteristics. Wheel torques are independently controlled using direct yaw moment and side slip control method to pro-actively improve vehicle handling. At high values of side slip the steering is no more capable of generating yaw moment and vehicle becomes laterally unstable. By unequal torque distribution a restoring yaw moment is generated and vehicle stability is ensured. The MPC computes the optimal couple traction/braking torque of the four in-wheel motors, from basic driving slogans, which are, steering angle and desired speed.

In recent times, electric vehicles (EV) are gaining a lot of attention as they run clean and are environment friendly. Recent advances in the applications of integrating control systems in automotive vehicles have made it practicable to accomplish improvement in vehicle's longitudinal and lateral dynamics. This paper deals with a brief overview of current state of art vehicle technologies like direct yaw moment control, traction control and side slip control of EV. There are various controller algorithms available in literature with different torque vectoring strategies. As EV can be precisely controlled because of quick in hub wheel motor response times, therefore various torque vectoring strategies can be comfortably used for enhancing vehicle dynamics. Moreover, by using four independent in-wheel motors, several types of motion controls can be performed.