Next-Cycle Optimal Fuel Control for Cycle-to-Cycle Variability Reduction in EGR-Diluted Combustion

Bryan P. Maldonado, Brian C. Kaul, Catherine D. Schuman, Steven R. Young, J. Parker Mitchell

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Dilute combustion using exhaust gas recirculation (EGR) is a cost-effective method for increasing engine efficiency. At high EGR levels, however, its efficiency benefits diminish as cycle-to-cycle variability (CCV) intensifies. In this simulation study, cycle-to-cycle fuel control was used to reduce CCV by injecting additional fuel in operating conditions with sporadic misfires and partial burns. An optimal control policy was proposed that utilizes 1) a physics-based model that tracks in-cylinder gas composition and 2) a one-step-ahead prediction of the combustion efficiency based on a kernel density estimator. The optimal solution, however, presents a tradeoff between the reduction in combustion CCV and the increase in fuel injection quantity required to stabilize the charge. Such a tradeoff can be adjusted by a single parameter embedded in the cost function. Simulation results indicated that combustion CCV can be reduced by as much as 65% by using at most 1% additional fuel. Although the control design presented here does not include fuel trim to maintain lambda = 1 for three-way catalyst compatibility, it is envisioned that this approach would be implemented alongside such an external controller, and the theoretical contribution presented here provides a first insight into the feasibility of CCV control using fuel injection.

Original languageEnglish
Article number9302635
Pages (from-to)2204-2209
Number of pages6
JournalIEEE Control Systems Letters
Volume5
Issue number6
DOIs
StatePublished - Dec 2021

Funding

Manuscript received September 14, 2020; revised November 30, 2020; accepted December 6, 2020. Date of publication December 22, 2020; date of current version April 28, 2021. This work was supported in part by the UT-Battelle LLC with the U.S. DOE under Contract DE-AC05-00OR22725; in part by the DOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office; and in part by the Laboratory Directed Research and Development Program of ORNL. Recommended by Senior Editor R. S. Smith. (Corresponding author: Bryan P. Maldonado.) Bryan P. Maldonado and Brian C. Kaul are with the Buildings and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830 USA (e-mail: [email protected]; [email protected]).

FundersFunder number
ORNL Laboratory Research and Development Program
U.S. Department of EnergyDE-AC05-00OR22725
Office of Energy Efficiency and Renewable Energy
UT-Battelle

    Keywords

    • Automotive control
    • energy systems
    • grey-box modeling
    • stochastic optimal control

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