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Data-driven real-time fuel cetane estimation and control design for multifuel UAVs

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  • Pal, Anuj
  • Cornelius, Andrew
  • Sun, Zongxuan
  • Kim, Kenneth
  • Kweon, Chol-Bum Mike

Abstract

Multifuel compression ignition (CI) engines have been proven to be useful for the aviation industry, especially for powering unmanned aerial vehicles (UAVs). To operate multifuel UAVs at desired operating conditions, an appropriate control strategy is needed that can adapt based on changing fuel properties. Therefore, having knowledge of fuel properties at all times is critical. However, the challenge comes in identifying fuel property in real-time when fuel switching happens during engine operation. The current work proposes a novel data-driven control algorithm for estimating fuel property (cetane number) and adapting the control strategy in real-time. Gaussian process regression (GPR) is used for modeling the multifuel CI engine, and feedforward control is developed to correct the control inputs based on the estimated fuel cetane property. For feedforward control development, model inversion of data-driven GPR is performed. Based on the estimated fuel property and using the feedforward table, the control strategy gets adjusted in real-time within one combustion cycle to avoid the system performance from drifting away beyond specified bounds. The entire approach was implemented and validated on an engine test bed. Fuel switching is performed in real time, and the results demonstrated the capability of the algorithm to maintain the desired performance.

Suggested Citation

  • Pal, Anuj & Cornelius, Andrew & Sun, Zongxuan & Kim, Kenneth & Kweon, Chol-Bum Mike, 2024. "Data-driven real-time fuel cetane estimation and control design for multifuel UAVs," Applied Energy, Elsevier, vol. 367(C).
    • Handle: RePEc:eee:appene:v:367:y:2024:i:c:s0306261924007190
    DOI: 10.1016/j.apenergy.2024.123336
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    References listed on IDEAS

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    1. Zeng, Tao & Xiao, Long & Chen, Jinrui & Li, Yu & Yang, Yi & Huang, Shulong & Deng, Chenghao & Zhang, Caizhi, 2023. "Feedforward-based decoupling control of air supply for vehicular fuel cell system: Methodology and experimental validation," Applied Energy, Elsevier, vol. 335(C).
    2. Xu, Bin & Li, Xiaoya, 2021. "A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications," Applied Energy, Elsevier, vol. 286(C).
    3. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    4. Bemani, Amin & Xiong, Qingang & Baghban, Alireza & Habibzadeh, Sajjad & Mohammadi, Amir H. & Doranehgard, Mohammad Hossein, 2020. "Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models," Renewable Energy, Elsevier, vol. 150(C), pages 924-934.
    5. Owoyele, Opeoluwa & Pal, Pinaki, 2021. "A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design," Applied Energy, Elsevier, vol. 285(C).
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