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Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application

Author

Listed:
  • Maarten Vlaswinkel

    (Control Systems Technology, Department of Mechanical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Frank Willems

    (Control Systems Technology, Department of Mechanical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
    Powertrains Department, TNO Mobility & Built Environment, 5700 AT Helmond, The Netherlands)

Abstract

Cylinder-pressure-based control is a key enabler for advanced pre-mixed combustion concepts. In addition to guaranteeing robust and safe operation, it allows for cylinder pressure and heat release shaping. This requires fast control-oriented combustion models. Over the years, mean-value models have been proposed that can predict combustion metrics (e.g., gross indicated mean effective pressure ( IMEP g ), or the crank angle where 50% of the total heat is released (CA50)) or models that predict the full in-cylinder pressure. However, these models are not able to capture cycle-to-cycle variations. The inclusion of the cycle-to-cycle variations is important in the control design for combustion concepts, like reactivity-controlled compression ignition, that can suffer from large cycle-to-cycle variations. In this study, the in-cylinder pressure and cycle-to-cycle variations are modelled using a data-based approach. The in-cylinder conditions and fuel settings are the inputs to the model. The model combines principal component decomposition and Gaussian process regression. A detailed study is performed on the effects of the different hyperparameters and kernel choices. The approach is applicable to any combustion concept, but is most valuable for advance combustion concepts with large cycle-to-cycle variation. The potential of the proposed approach is successfully demonstrated for a reactivity-controlled compression ignition engine running on diesel and E85. The average prediction error of the mean in-cylinder pressure over a complete combustion cycle is 0.051 bar and of the corresponding mean cycle-to-cycle variation is 0.24 bar 2 . This principal-component-decomposition-based approach is an important step towards in-cylinder pressure shaping. The use of Gaussian process regression provides important information on cycle-to-cycle variation and provides next-cycle control information on safety and performance criteria.

Suggested Citation

  • Maarten Vlaswinkel & Frank Willems, 2024. "Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application," Energies, MDPI, vol. 17(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1881-:d:1376056
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    References listed on IDEAS

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    1. Duarte Souza Alvarenga Santos, Nathália & Rückert Roso, Vinícius & Teixeira Malaquias, Augusto César & Coelho Baêta, José Guilherme, 2021. "Internal combustion engines and biofuels: Examining why this robust combination should not be ignored for future sustainable transportation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    2. Paykani, Amin & Garcia, Antonio & Shahbakhti, Mahdi & Rahnama, Pourya & Reitz, Rolf D., 2021. "Reactivity controlled compression ignition engine: Pathways towards commercial viability," Applied Energy, Elsevier, vol. 282(PA).
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