A Hierarchical Economic Model Predictive Controller That Exploits Look-Ahead Information of Roads to Boost Engine Performance

Zihao Liu, A.M. Dizqah, Jose Martin Herreros, Joschka Schaub, Olivier Haas

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Abstract

Sensors and communication capabilities of connected vehicles provide look-ahead information that can be exploited by vehicle controllers. This work demonstrates the benefits of look-ahead information combined with hierarchical economic model predictive control for the airpath management of compression ignition engines. This work exploits road information predicted with a 0.1-and 2-s horizon to simultaneously control fast and slow engine dynamics, respectively. It controls the variable nozzle turbocharger and dual-loop exhaust gas recirculation, at a 0.01-s rate, to simultaneously optimize NOx, soot, and fuel economy. Simulation studies and hardware-in-loop implementation on an ARM Cortex-A15 processor demonstrate improved NOx, soot, and torque tracking without compromising fuel economy, and a worst case computation time of 8.92 ms.
Original languageEnglish
Pages (from-to)2632-2643
Number of pages12
JournalIEEE Transactions on Control Systems Technology
Volume31
Issue number6
Early online date20 Jun 2023
DOIs
Publication statusPublished - Nov 2023

Bibliographical note

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

Funder

This work was supported in part by Coventry University, U.K., and in part by FEV GmbH.

Keywords

  • Airpath control
  • economic model predictive controller (eMPC)
  • exhaust gas recirculation (EGR)
  • look-ahead control
  • model predictive control (MPC)
  • variable nozzle turbocharger (VNT)

ASJC Scopus subject areas

  • Control and Systems Engineering

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