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A fully automated smooth calibration generation methodology for optimization of latest generation of automotive diesel engines

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

Abstract

The calibration of modern automotive diesel engines, stored in form of maps in the engine control unit, must fulfill stringent requirements in terms of smoothness, ensuring a subtle transition of control parameters between neighbor operating points. However, this could lead to penalties in emissions or fuel consumption. It is therefore necessary to develop a methodology capable of carrying out the engine calibration task in a quick and automatic way. In this paper, an original fully automated methodology for the generation of smooth calibration maps is proposed. Starting from a population of more than 80 optimized calibrations for 20 engine operating points, generated by means of a genetic algorithm-based multi objective optimizer, a final calibration was then selected in an automated way, on the basis of a trade-off between the performance of the calibration and the smoothness of maps. The results achieved clearly showed that in comparison with traditional methods similar level of smoothness can be achieved while having 5–10% lower NOx and soot emissions with an additional benefit of 1% in fuel consumption. Furthermore, the time required for the calibration task of an automotive diesel engine can be reduced by more than half.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:178:y:2019:i:c:p:334-343
    DOI: 10.1016/j.energy.2019.04.122
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    References listed on IDEAS

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    1. Wong, Ka In & Wong, Pak Kin & Cheung, Chun Shun & Vong, Chi Man, 2013. "Modeling and optimization of biodiesel engine performance using advanced machine learning methods," Energy, Elsevier, vol. 55(C), pages 519-528.
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    3. 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.
    4. Singh, Yashvir & Sharma, Abhishek & Tiwari, Sumit & Singla, Amneesh, 2019. "Optimization of diesel engine performance and emission parameters employing cassia tora methyl esters-response surface methodology approach," Energy, Elsevier, vol. 168(C), pages 909-918.
    5. Knecht, Walter, 2008. "Diesel engine development in view of reduced emission standards," Energy, Elsevier, vol. 33(2), pages 264-271.
    6. Millo, Federico & Giacominetto, Paolo Ferrero & Bernardi, Marco Gianoglio, 2012. "Analysis of different exhaust gas recirculation architectures for passenger car Diesel engines," Applied Energy, Elsevier, vol. 98(C), pages 79-91.
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