Abstract
Driving on highways and arterial roadways involves vehicle acceleration, braking, cruising, coasting, and idling episodes. As the vehicle speed deviates from its “fuel optimum speed,” additional fuel is consumed, thus reducing the vehicle fuel efficiency. The research presented in this article develops a connected vehicle application entitled Eco-Cooperative Adaptive Cruise Control (ECACC) that uses infrastructure-to-vehicle (I2V) communication to receive signal phasing and timing (SPaT) data, predict future constraints on a vehicle's trajectory, and optimize its trajectory to minimize the vehicle's fuel consumption level. The trajectory optimization is made using a moving horizon dynamic programming (DP) approach. A modified A-star algorithm is developed to enhance the computational efficiency of the DP for use in real-time implementations. The model is calibrated and tested on 30 top-sold vehicles in the United States and is demonstrated to provide fuel savings within the vicinity of signalized intersections in the range of 5 to 30%.
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