Receding Horizon Reference Governor for Implementable and Optimal Powertrain-Aware Eco-Driving ?

摘要

This paper presents an adaptive, two-level control structure that makes it possible to implement a numerical optimization algorithm for eco-driving in real time. The reference governor in the higher level is characterized by a low, flexible sampling rate and adopts a receding horizon for preview and optimization. The optimization algorithm thereby finds the energy-minimizing solution, based on Pontryagin’s minimum principle (PMP), for traveling the selected route segments without colliding with the preceding vehicle. The tracking control in the lower level compensates for errors due to modeling inaccuracies and unmodeled disturbances. The hierarchical structure can accommodate different types of numerical solvers and control schemes that apply to various vehicle powertrain configurations. A large-scale simulation study using real-world route data with high-fidelity powertrain models validates the proposed control structure and its online implementation.