Cloud-Based Centralized Platoon Predictive Cruise Control Considering Fuel-Efficient in Highway

Previous research ignores the integration of the states of all vehicles in the platoon and static road information and uses only the lead vehicle or the average state for planning, which will lose the optimality of the platoon economy. The platoon's distributed optimization strategy is difficult to satisfy the optimality of the two-layer optimizer simultaneously. In this paper, we propose the centralized cloud-based platoon predictive cruise control (C-CPPCC) for highway scenarios, enhancing the platoon's efficiency and stability, while addressing the issue of increasing fuel consumption in existing platoon predictive cruise control (PPCC) systems. The C-CPPCC employs a centralized architecture, integrating the status information of platoon vehicles in the cloud to create a longitudinal planning model. It considers platoon stability and economical driving, introducing a centralized PPCC algorithm with rolling distance domains. The method's effectiveness was demonstrated by simulations using real vehicle and road data. Compared to the predecessor-leader following cruise control platoon (PLF-CC) and the distributed cloud-based PPCC (D-CPPCC), the C-CPPCC reduces fuel consumption by 7.52% and 1.57%, respectively. The fuel consumption increase for platoon-following vehicles is significantly reduced with the proportion from 9.25% and 1.59% to 0.07%. Simulation results verify the effectiveness of the proposed strategy.