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A New Control-Oriented Semi-Empirical Approach to Predict Engine-Out NOx Emissions in a Euro VI 3.0 L Diesel Engine

Author

Listed:
  • Roberto Finesso

    (Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Gilles Hardy

    (FPT Motorenforschung AG, Schlossgasse 2, 9320 Arbon, Switzerland)

  • Claudio Maino

    (Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Omar Marello

    (Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Ezio Spessa

    (Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

Abstract

The present study is focused on the development of a new control-oriented semi-empirical model to predict nitrogen oxide (NOx) emissions in a light-duty diesel engine under both steady-state and transient conditions. The model is based on the estimation of the deviations of NOx emissions, with respect to the nominal engine-calibration map values, as a function of the deviations of the intake oxygen concentration and of the combustion phasing. The model also takes into account the effects of engine speed, total injected quantity, and ambient temperature and humidity. The approach has been developed and assessed on an Fiat Powertrain Technologies (FPT) Euro VI 3.0 L diesel engine for light-duty applications, in the frame of a research project in collaboration with FPT Industrial. The model has also been tested on a rapid prototyping device, and it was shown that it requires a very short computational time, thus being suitable for implementation on the Engine Control Unit (ECU) for real-time NOx control tasks.

Suggested Citation

  • Roberto Finesso & Gilles Hardy & Claudio Maino & Omar Marello & Ezio Spessa, 2017. "A New Control-Oriented Semi-Empirical Approach to Predict Engine-Out NOx Emissions in a Euro VI 3.0 L Diesel Engine," Energies, MDPI, vol. 10(12), pages 1-26, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1978-:d:120909
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    References listed on IDEAS

    as
    1. Seungha Lee & Youngbok Lee & Gyujin Kim & Kyoungdoug Min, 2017. "Development of a Real-Time Virtual Nitric Oxide Sensor for Light-Duty Diesel Engines," Energies, MDPI, vol. 10(3), pages 1-21, March.
    2. d’Ambrosio, Stefano & Finesso, Roberto & Fu, Lezhong & Mittica, Antonio & Spessa, Ezio, 2014. "A control-oriented real-time semi-empirical model for the prediction of NOx emissions in diesel engines," Applied Energy, Elsevier, vol. 130(C), pages 265-279.
    3. Asprion, Jonas & Chinellato, Oscar & Guzzella, Lino, 2013. "A fast and accurate physics-based model for the NOx emissions of Diesel engines," Applied Energy, Elsevier, vol. 103(C), pages 221-233.
    4. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    5. Gyujin Kim & Sunyoung Moon & Seungha Lee & Kyoungdoug Min, 2017. "Numerical Analysis of the Combustion and Emission Characteristics of Diesel Engines with Multiple Injection Strategies Using a Modified 2-D Flamelet Model," Energies, MDPI, vol. 10(9), pages 1-17, August.
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    1. Bolu, Sencer & Ozgul, Emre & Epguzel, Emre & Gurel, Cetin, 2022. "Use of thermodynamic models for compression ratio and peak firing pressure optimization in heavy-duty diesel engine," Energy, Elsevier, vol. 248(C).

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