Improving Handling Performance of an Electric Vehicle Using Model Predictive Control

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. The reference trajectories of yaw rate and side slip angle are also inputted in the controller. The controller output is four wheel torques which are fed to the vehicle. The vehicle is modelled in ADAMS-Car® to incorporate nonlinear suspension dynamics, compliance effects, roll and pitch motions, sophisticated tire model, etc. which are the major limitations of a single track bicycle model. The vehicle model is exported in state space form to the MATLAB® environment to be integrated with the control model. The controller is able to track the reference yaw rate and desired trajectory with negligible side slip. Simulation results with control application have been discussed for a lane change event.