Real-time fuel consumption and NO x emissions coordinated control for intelligent vehicles

This study presents a real-time coordinated control strategy for optimizing fuel efficiency and NO x emissions in commercial vehicles operating in urban environments. First, data-fitting vehicle-level models are developed to characterize fuel consumption, NO x emissions, and after-treatment system conversion efficiency, accounting for the coupled dynamics of the engine and exhaust treatment system. Next, a real-time optimization framework is established by integrating traffic signal information, preceding vehicle states, and system dynamics. To enable rapid computation, a hybrid reasoning-iteration approach based on Pontryagins maximum principle is proposed, significantly reducing computational complexity while yielding an explicit solution. Simulation results demonstrate the effectiveness of the proposed method, and hardware-in-the-loop experiments further validate its practical feasibility.