Economically Optimized Distributed Model Predictive Control for Connected Vehicles Using Buffer Functions

The cooperation among multiple connected vehicles (CVs) enhances the swarm intelligence of the transportation systems, significantly improving their overall performance. To optimize fuel consumption in vehicle fleet movements, this study proposes a fuel economy optimization approach for a fleeting vehicle swarm using improved distributed model predictive control (DMPC). In the DMPC framework, each CV shares its predicted trajectory within the predictive horizon with its neighboring CVs at each control loop. Using both the own and the neighbors' predicted trajectories, each CV solves an open-loop control optimization problem for fleet formation. In this process, a buffer function is incorporated into the cost function in DMPC to prevent frequent spacing adjustments caused by minor errors, thereby reducing fuel consumption. Simulation results demonstrate enhanced energy efficiency of CV fleets.