Predictive Mean Field Game Approach to Energy Saving Problems of Large-Population HEVs With Broadcast Traffic Information

In the past decades, the energy optimization strategy for human-driver hybrid electric vehicles (HEVs) has been widely investigated to improve fuel economy. With the highly increasing penetration of connected and automated HEVs, fuel consumption reduction can be achieved further. This paper explores the potential to reduce the whole fuel consumption by jointly optimizing speed dynamic and HEV powertrain operation for a large population of connected and automated HEVs. To avoid the computation burden of the large population of HEVs, a novel decentralized control scheme is designed by employing the mean field game (MFG) theory. Thanks to the communication by vehicle to everything (V2X) that the real-time velocity distribution of the large population of HEVs is available for each HEV, the receding horizon sense is introduced to MFG to avoid the open-loop prediction error of velocity distribution within the optimization horizon. The solution is derived from a novel numerical method for the general nonlinear MFG problem. A virtual connected traffic simulation platform is built in MATLAB/Simulink, and simulations are conducted to show the effectiveness of the proposed strategy.