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

February, 2022

IEEE Transactions on Control Systems Technology

https://doi.org/10.1109/TCST.2022.3149423

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Abstract

Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition (SI) engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit. Given the complexity and chaotic nature of combustion events, the controller was enhanced with online learning in order to identify the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events. This study showed that a kernel density estimator (KDE) can be used to learn the combustion properties in real time and can be incorporated into the feedback policy in order to calculate the optimal control command. Experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. Additionally, the proposed controller can achieve a large CCV reduction with less fuel enrichment compared to previous methods, overall contributing to an increase in 0.2% indicated fuel conversion efficiency.

Citation Information

Text

author     B. P. Maldonado and B. C. Kaul and C. D. Schuman and S. R. Young and J. P. Mitchell
title      Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle 
           Variability Using Kernel Density Estimators
journal    IEEE Transactions on Control Systems Technology
year       2022
doi        10.1109/TCST.2022.3149423
url        https://doi.org/10.1109/TCST.2022.3149423
pages      1-17

Bibtex

@ARTICLE{mks:22:nco,
    author = "B. P. Maldonado and B. C. Kaul and C. D. Schuman and S. R. Young and J. P. Mitchell",
    title = "Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle 
                Variability Using Kernel Density Estimators",
    journal = "IEEE Transactions on Control Systems Technology",
    year = "2022",
    doi = "10.1109/TCST.2022.3149423",
    url = "https://doi.org/10.1109/TCST.2022.3149423",
    pages = "1-17"
}