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

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.

Automated summary

This work demonstrates the benefits of utilizing look-ahead information and hierarchical economic model predictive control for optimizing the airpath management of compression ignition engines in connected vehicles. By exploiting predicted road information with different time horizons, it simultaneously controls fast and slow engine dynamics, optimizing NOx, soot, and fuel economy. Simulation studies and hardware-in-loop implementation show improved tracking performance and a worst-case computation time of 8.92 ms, without compromising fuel economy.