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Controlling the optimal combustion phasing in an HCCI engine based on load demand and minimum emissions

Author

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  • Nazoktabar, Mohsen
  • Jazayeri, Seyed Ali
  • Parsa, Mohsen
  • Ganji, Davoud Domiri
  • Arshtabar, Kamran

Abstract

Despite the potential benefits of the Homogenous Charge Compression Ignition (HCCI) engines, controlling the combustion phasing and emissions are the main obstacles in its development but, in conventional engines the load demand is simply controlled by accelerator. The aim of present work is developing a HCCI engine controller which controls its main parameter similar to conventional engine: controlling the combustion phasing based on load demand. In the present study, A Multi-Input Multi-Output (MIMO) controller has been designed and validated which is used to control HCCI engine main parameters such as: optimum combustion phasing, engine load and minimum emissions. Similar to conventional IC engine the controller is capable of tracking all desired set-points solely by means of load demand as the only reference trajectory. A physic-based control model together with application of Artificial Neural Networks (ANN) is developed to predict the optimum combustion phasing with minimum emissions based on load demand. The results of proposed model are validated with the experimental data for steady state and transient cases. The optimal CA50 is selected based on minimizing the emissions using a multi-zone kinetic coupled with a genetic algorithm. The developed controller performance has been tested thoroughly to evaluate the tracking and disturbance rejection capabilities. Results show that it is capable of rejecting the disturbances for fix engine loads. The controller is quick to reject the disturbances within 3–5 engine cycles, while deviations within 0.04 bar, 0.5CAD and 0.03 for IMEP, CA50 and emissions respectively.

Suggested Citation

  • Nazoktabar, Mohsen & Jazayeri, Seyed Ali & Parsa, Mohsen & Ganji, Davoud Domiri & Arshtabar, Kamran, 2019. "Controlling the optimal combustion phasing in an HCCI engine based on load demand and minimum emissions," Energy, Elsevier, vol. 182(C), pages 82-92.
    • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:82-92
    DOI: 10.1016/j.energy.2019.06.012
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    Cited by:

    1. Wan, Peng & Liu, Bolan & Li, Ben & Liu, Fanshuo & Zhang, Junwei & Fan, Wenhao & Tang, Jingxian, 2023. "Engine modelling architecture study for hybrid electric vehicle diagnosis application," Energy, Elsevier, vol. 282(C).
    2. Zhao, Hang & Liao, Zengbu & Liu, Jinxin & Li, Ming & Liu, Wei & Wang, Lei & Song, Zhiping, 2022. "A highly robust thrust estimation method with dissimilar redundancy framework for gas turbine engine," Energy, Elsevier, vol. 245(C).
    3. Baek, Seungju & Lee, Hyeonjik & Lee, Kihyung, 2021. "Fuel efficiency and exhaust characteristics of turbocharged diesel engine equipped with an electric supercharger," Energy, Elsevier, vol. 214(C).
    4. Kale, Aneesh Vijay & Krishnasamy, Anand, 2023. "Numerical investigation on selecting appropriate piston bowl geometry and compression ratio for gasoline-fuelled homogeneous charge compression ignited light-duty diesel engine," Energy, Elsevier, vol. 282(C).
    5. Fridrichová, K. & Drápal, L. & Vopařil, J. & Dlugoš, J., 2021. "Overview of the potential and limitations of cylinder deactivation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).

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