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Optimization of automotive diesel engine calibration using genetic algorithm techniques

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  • Millo, Federico
  • Arya, Pranav
  • Mallamo, Fabio

Abstract

Although the advancements in automotive diesel engines in the last two decades have resulted in the possibility of achieving better performance with lower pollutant emissions and fuel consumption, the increased complexity of the system and the high number of control parameters require the solution of optimization problems of high dimensionality. It is of crucial importance to identify suitable methodologies, which allow achieving the full exploitation of the potential of these powertrains. In this paper, an original methodology for optimizing the latest generation of common rail automotive diesel engines has been presented.

Suggested Citation

  • Millo, Federico & Arya, Pranav & Mallamo, Fabio, 2018. "Optimization of automotive diesel engine calibration using genetic algorithm techniques," Energy, Elsevier, vol. 158(C), pages 807-819.
    • Handle: RePEc:eee:energy:v:158:y:2018:i:c:p:807-819
    DOI: 10.1016/j.energy.2018.06.044
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    References listed on IDEAS

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    1. Knecht, Walter, 2008. "Diesel engine development in view of reduced emission standards," Energy, Elsevier, vol. 33(2), pages 264-271.
    2. Beatrice, Carlo & Napolitano, Pierpaolo & Guido, Chiara, 2014. "Injection parameter optimization by DoE of a light-duty diesel engine fed by Bio-ethanol/RME/diesel blend," Applied Energy, Elsevier, vol. 113(C), pages 373-384.
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    Cited by:

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    2. Duro, João A. & Ozturk, Umud Esat & Oara, Daniel C. & Salomon, Shaul & Lygoe, Robert J. & Burke, Richard & Purshouse, Robin C., 2023. "Methods for constrained optimization of expensive mixed-integer multi-objective problems, with application to an internal combustion engine design problem," European Journal of Operational Research, Elsevier, vol. 307(1), pages 421-446.
    3. Yu, Xunzhao & Zhu, Ling & Wang, Yan & Filev, Dimitar & Yao, Xin, 2022. "Internal combustion engine calibration using optimization algorithms," Applied Energy, Elsevier, vol. 305(C).
    4. Yindong Song & Yiyu Xu & Xiuwei Cheng & Ziyu Wang & Weiqing Zhu & Xinyu Fan, 2022. "Using a Genetic Algorithm to Achieve Optimal Matching between PMEP and Diameter of Intake and Exhaust Throat of a High-Boost-Ratio Engine," Energies, MDPI, vol. 15(5), pages 1-17, February.
    5. Pan, Jeng-Shyang & Hu, Pei & Chu, Shu-Chuan, 2021. "Binary fish migration optimization for solving unit commitment," Energy, Elsevier, vol. 226(C).
    6. Ferrari, A. & Novara, C. & Paolucci, E. & Vento, O. & Violante, M. & Zhang, T., 2018. "Design and rapid prototyping of a closed-loop control strategy of the injected mass for the reduction of CO2, combustion noise and pollutant emissions in diesel engines," Applied Energy, Elsevier, vol. 232(C), pages 358-367.
    7. Arya, Pranav & Millo, Federico & Mallamo, Fabio, 2019. "A fully automated smooth calibration generation methodology for optimization of latest generation of automotive diesel engines," Energy, Elsevier, vol. 178(C), pages 334-343.
    8. Tadros, M. & Ventura, M. & Guedes Soares, C., 2019. "Optimization procedure to minimize fuel consumption of a four-stroke marine turbocharged diesel engine," Energy, Elsevier, vol. 168(C), pages 897-908.
    9. Gharehghani, Ayat & Abbasi, Hamid Reza & Alizadeh, Pouria, 2021. "Application of machine learning tools for constrained multi-objective optimization of an HCCI engine," Energy, Elsevier, vol. 233(C).

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