Design and Performance Analysis of a Cascaded Model Predictive Controller and Command Governor for Fuel-Efficient Control of Heavy-Duty Trucks

This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20-40 s, reducing fuel consumption by 4-6% when compared to a Gipps car-following model.