Robust Hierarchical MPC for Handling Long Horizon Demand Forecast Uncertainty with Application to Automotive Thermal Management

TL;DR

This paper introduces a robust hierarchical MPC framework that effectively manages long-term demand forecast uncertainties in dynamic systems, demonstrated through automotive thermal management simulations.

Contribution

The paper proposes a novel robust hierarchical MPC with a constraint tightening approach utilizing preview information to improve long-term demand uncertainty handling.

Findings

Enhanced robustness against demand forecast errors

Effective long-horizon demand management in vehicle systems

Simulation confirms improved constraint satisfaction

Abstract

This paper presents a robust hierarchical MPC (H-MPC) for dynamic systems with slow states subject to demand forecast uncertainty. The H-MPC has two layers: (i) the scheduling MPC at the upper layer with a relatively long prediction/planning horizon and slow update rate, and (ii) the piloting MPC at the lower layer over a shorter prediction horizon with a faster update rate. The scheduling layer MPC calculates the optimal slow states, which will be tracked by the piloting MPC, while enforcing the system constraints according to a long-range and approximate prediction of the future demand/load, e.g., traction power demand for driving a vehicle. In this paper, to enhance the H-MPC robustness against the long-term demand forecast uncertainty, we propose to use the high-quality preview information enabled by the connectivity technology over the short horizon to modify the planned trajectories via a constraint tightening approach at the scheduling layer. Simulation results are presented for a simplified vehicle model to confirm the effectiveness of the proposed robust H-MPC framework in handling demand forecast uncertainty.