An energy and emission conscious adaptive cruise controller for a connected automated diesel truck

ABSTRACT

Conventional speed planning for connected automated vehicles (CAVs) adopts an energy-centric perspective and improves fuel economy by means of reducing the power loss due to braking and operating the engine at its high efficiency region. This paper considers a simulated connected automated truck with a diesel powertrain and a selective catalytic reduction (SCR) system for the treatment of NOx emissions, and first shows that a 20% fuel economy improvement is followed by up to 50% increase in NOx emissions with conventional speed planning due to its sole focus on energy that neglects emissions. Then, a novel model predictive controller (MPC) is designed for concurrent treatment of energy and emissions within speed planning of the CAV. Details of this energy and emissions conscious (E${}^2$C) MPC design are described, including the vehicle, powertrain and emission model development, selection of the appropriate objective function and its parameters for acceptable optimality, and computational performance. Simulation results of the E${}^2$C-MPC over multiple drive cycles are presented to demonstrate the robust performance of the controller. The results show 5–15% improvement in the fuel economy with a corresponding 0–25% reduction in NOx emissions for different drive cycles without requiring re-calibration for each test cycle.

KEYWORDS:

• Connected automated vehicles
• model predictive control
• diesel powertrain
• fuel efficiency
• NOx emissions control
• robustness to drive cycle

Acknowledgements

The authors would like to thank Michiel Van Nieuwstadt and Cory Hendrickson from Ford Research and Advanced Engineering for providing valuable data and technical suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Tulga Ersal http://orcid.org/0000-0002-6811-8529

Additional information

Funding

This material is based upon work supported by the National Science Foundation under Grant No. 1646019; Division of Computer and Network Systems.