Engine-Map-Based Predictive Fuel-Efficient Control Strategies for a Group of Connected Vehicles

An engine-map-based predictive fuel-efficient control strategy for a group of connected vehicles is presented. A decentralized model predictive control framework is formulated to predict the optimal velocity profile that compromises fuel economy and mobility while guaranteeing the safety of each vehicle. In the model predictive control framework, an engine-map-based fuel consumption model is established by implementing a backward conventional vehicle model in the cost function. Moreover, the cost function is normalized by dividing each term by its reference value. An extra cost is added to the safety term when the distance between adjacent vehicles drops to a critical value to guarantee vehicle safety, while another extra cost is considered for the velocity tracking term to prevent the violation of traffic rules. The results of simulation show the effectiveness of the proposed control method.