Co-simulation Architecture and Platform Establishment Method for Cloud-Based Predictive Cruise Control System

The Cloud-Based Predictive Cruise Control (CPCC) system obtain road and vehicle data from the Cloud Control Platform (CCP) and efficiently computes the optimal speed and trajectory for intelligent connected vehicles (ICVs). It holds significant potential for conserving vehicle energy and minimizing waiting times. However, existing research on CPCC often overlooks the practical feasibility from theory to application and lacks effective validation regarding the consistency and rationality between theoretical architecture and model application. In this paper, a co-simulation method for CPCC is proposed. Firstly, the overall architecture of the CPCC system is proposed, outlining the real-time acquisition of road information and the speed planning strategy for ICVs. Then, models and algorithms for road and vehicles within the CPCC system are introduced. Finally, a CPCC co-simulation platform is established to validate the collaborative feasibility of the CPCC architecture, models, and algorithms. Simulation results show that compared to scenarios without CPCC, the CPCC-controlled ICVs experience a 2% reduction in cruising time and a 14.7% decrease in fuel consumption. Additionally, ICV co-control simulation results indicate a 4.05% decrease in average queue length at intersections, a 4.58% reduction in average vehicle delay time, an 8.99% decrease in average number of stops, a 12.89% reduction in average vehicle energy consumption, a 7.81% decrease in CO emissions, an 8.08% reduction in NOx emissions, and a 7.46% decrease in VOC emissions.