Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators
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.
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1847545
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Control Systems Technology
- Additional Journal Information:
- Journal Volume: 0; Journal Issue: 0; Journal ID: ISSN 1063-6536
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 33 ADVANCED PROPULSION SYSTEMS; Internal combustion engines; Stochastic systems; Statistical learning; Optimal control; Kernel density estimation
Citation Formats
Maldonado Puente, Bryan, Kaul, Brian, Schuman, Catherine, Young, Steven, and Mitchell, Parker. Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators. United States: N. p., 2022.
Web. doi:10.1109/TCST.2022.3149423.
Maldonado Puente, Bryan, Kaul, Brian, Schuman, Catherine, Young, Steven, & Mitchell, Parker. Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators. United States. https://doi.org/10.1109/TCST.2022.3149423
Maldonado Puente, Bryan, Kaul, Brian, Schuman, Catherine, Young, Steven, and Mitchell, Parker. Tue .
"Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators". United States. https://doi.org/10.1109/TCST.2022.3149423. https://www.osti.gov/servlets/purl/1847545.
@article{osti_1847545,
title = {Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators},
author = {Maldonado Puente, Bryan and Kaul, Brian and Schuman, Catherine and Young, Steven and Mitchell, Parker},
abstractNote = {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.},
doi = {10.1109/TCST.2022.3149423},
journal = {IEEE Transactions on Control Systems Technology},
number = 0,
volume = 0,
place = {United States},
year = {Tue Feb 01 00:00:00 EST 2022},
month = {Tue Feb 01 00:00:00 EST 2022}
}
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