Modelling and Analysis of a Cooperative Adaptive Cruise Control (CACC) Algorithm for Fuel Economy

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model. Simulations were run to model and test the Eco-CACC algorithms with different lead vehicle driving behaviors. The performance of the new Eco-CACC model is then compared to a Proportional Derivative (PD) based Adaptive Cruise Control (ACC) system that aimed to follow the lead vehicle as closely as possible. The PD controller was tuned for nominal performance. The preliminary results show that the proposed CACC model was able to decrease the rate of acceleration and decelerations experienced by the ego vehicle to attain a smooth speed profile that consumed less fuel than its PD-controlled ACC counterpart.