ECO-cruise control using economic model predictive control

Combining advanced driver assistance systems with V2X communications shows a high potential to increase the driving safety and reduce vehicular energy consumption. This paper presents an ECO Cruise Control (ECC) system based on economic model predictive control for reducing the energy consumption of electric vehicles using traffic information. The ECC comprises of a quadratic cost function, which is obtained from a fitted power map of an electric motor. The longitudinal dynamic model of the controller is formulated in a position based form in order to apply a position dependent preview information, e.g., traffic light phases, velocity limits, and elevation profiles of roads. Utilizing all this information processed as control constraints and external disturbances, the ECC system computes an acceleration profile that minimizes energy usage and satisfies position-varying constraints simultaneously. The controller is tested on a nonlinear vehicle model with uphill and downhill road sections. Results show that the proposed approach reduces vehicular energy consumption on hilly roads and the optimization problem can be solved in real-time.