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

IEEE Transactions on Control Systems Technology, vol. 31, pp. 2632–2643

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.

Authors 5

1. Zihao Liu

   Coventry University

   Affiliation as printed

   Centre of Future Transport and Cities, Coventry University, Coventry, U.K

   YRobot Inc, Suzhou, China

2. Arash M. Dizqah

   University of Sussex

   Affiliation as printed

   School of Engineering and Informatics, University of Sussex, Brighton, U.K

3. J.M. Herreros

   University of Birmingham

   Affiliation as printed

   Department of Mechanical Engineering, University of Birmingham, Birmingham, U.K

4. Joschka Schaub Aachen FEV Europe GmbH

   FEV (Germany)

   Affiliation as printed

   FEV Europe GmbH, Aachen, Germany

5. Olivier C. L. Haas

   Coventry University

   Affiliation as printed

   Centre of Future Transport and Cities, Coventry University, Coventry, U.K