Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Maldonado Puente, Bryan; Kaul, Brian; Schuman, Catherine; Young, Steven; Mitchell, Parker

doi:10.1109/TCST.2022.3149423

Title: Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Journal Article · 31 January 2022 · IEEE Transactions on Control Systems Technology

DOI: https://doi.org/10.1109/TCST.2022.3149423 · OSTI ID:1847545

^[1];

^[1]; Mitchell, Parker ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1847545

Journal Information:: IEEE Transactions on Control Systems Technology, Journal Name: IEEE Transactions on Control Systems Technology Journal Issue: 0 Vol. 0; ISSN 1063-6536

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Title: Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

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