IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i4p1107-d502210.html
   My bibliography  Save this article

Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping

Author

Listed:
  • Stefano d’Ambrosio

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

  • 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)

  • Andrea Manelli

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

  • Alessandro Mancarella

    (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)

  • Antonio Mittica

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

Abstract

In the present paper, a model-based controller of engine torque and engine-out Nitrogen oxide (NOx) emissions, which was previously developed and tested by means of offline simulations, has been validated on a FPT F1C 3.0 L diesel engine by means of rapid prototyping. With reference to the previous version, a new NO x model has been implemented to improve robustness in terms of NO x prediction. The experimental tests have confirmed the basic functionality of the controller in transient conditions, over different load ramps at fixed engine speeds, over which the average RMSE (Root Mean Square Error) values for the control of NO x emissions were of the order of 55–90 ppm, while the average RMSE values for the control of brake mean effective pressure (BMEP) were of the order of 0.25–0.39 bar. However, the test results also highlighted the need for further improvements, especially concerning the effect of the engine thermal state on the NO x emissions in transient operation. Moreover, several aspects, such as the check of the computational time, the impact of the controller on other pollutant emissions, or on the long-term engine operations, will have to be evaluated in future studies in view of the controller implementation on the engine control unit.

Suggested Citation

  • Stefano d’Ambrosio & Roberto Finesso & Gilles Hardy & Andrea Manelli & Alessandro Mancarella & Omar Marello & Antonio Mittica, 2021. "Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping," Energies, MDPI, vol. 14(4), pages 1-25, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1107-:d:502210
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/4/1107/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/4/1107/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fabio Cococcetta & Roberto Finesso & Gilles Hardy & Omar Marello & Ezio Spessa, 2019. "Implementation and Assessment of a Model-Based Controller of Torque and Nitrogen Oxide Emissions in an 11 L Heavy-Duty Diesel Engine," Energies, MDPI, vol. 12(24), pages 1-19, December.
    2. Edgar Talavera & Alberto Díaz-Álvarez & Felipe Jiménez & José E. Naranjo, 2018. "Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation," Energies, MDPI, vol. 11(1), pages 1-17, January.
    3. Stefano d’Ambrosio & Alessandro Ferrari & Alessandro Mancarella & Salvatore Mancò & Antonio Mittica, 2019. "Comparison of the Emissions, Noise, and Fuel Consumption Comparison of Direct and Indirect Piezoelectric and Solenoid Injectors in a Low-Compression-Ratio Diesel Engine," Energies, MDPI, vol. 12(21), pages 1-16, October.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alessandro Mancarella & Omar Marello, 2022. "Effect of Coolant Temperature on Performance and Emissions of a Compression Ignition Engine Running on Conventional Diesel and Hydrotreated Vegetable Oil (HVO)," Energies, MDPI, vol. 16(1), pages 1-27, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alessandro Mancarella & Omar Marello, 2022. "Effect of Coolant Temperature on Performance and Emissions of a Compression Ignition Engine Running on Conventional Diesel and Hydrotreated Vegetable Oil (HVO)," Energies, MDPI, vol. 16(1), pages 1-27, December.
    2. Yi Dong & Jianmin Liu & Yanbin Liu & Xinyong Qiao & Xiaoming Zhang & Ying Jin & Shaoliang Zhang & Tianqi Wang & Qi Kang, 2020. "A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment," Energies, MDPI, vol. 13(1), pages 1-24, January.
    3. Gu, Jie & Wang, Yingyuan & Hu, Jiancun & Zhang, Kun & Shi, Lei & Deng, Kangyao, 2024. "Real-time prediction of fuel consumption and emissions based on deep autoencoding support vector regression for cylinder pressure-based feedback control of marine diesel engines," Energy, Elsevier, vol. 300(C).
    4. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    5. Li, Haijian & Zhang, Junjie & Sun, Xiaoliang & Niu, Jun & Zhao, Xiaohua, 2022. "A survey of vehicle group behaviors simulation under a connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    6. Sasanka Katreddi & Sujan Kasani & Arvind Thiruvengadam, 2022. "A Review of Applications of Artificial Intelligence in Heavy Duty Trucks," Energies, MDPI, vol. 15(20), pages 1-20, October.
    7. Zamboni, Giorgio & Moggia, Simone & Capobianco, Massimo, 2016. "Hybrid EGR and turbocharging systems control for low NOX and fuel consumption in an automotive diesel engine," Applied Energy, Elsevier, vol. 165(C), pages 839-848.
    8. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    9. Deb, Madhujit & Paul, Abhishek & Debroy, Durbadal & Sastry, G.R.K. & Panua, Raj Sekhar & Bose, P.K., 2015. "An experimental investigation of performance-emission trade off characteristics of a CI engine using hydrogen as dual fuel," Energy, Elsevier, vol. 85(C), pages 569-585.
    10. Loris Ventura & Roberto Finesso & Stefano A. Malan, 2023. "Development of a Model-Based Coordinated Air-Fuel Controller for a 3.0 dm 3 Diesel Engine and Its Assessment through Model-in-the-Loop," Energies, MDPI, vol. 16(2), pages 1-23, January.
    11. Shahir, V.K. & Jawahar, C.P. & Suresh, P.R., 2015. "Comparative study of diesel and biodiesel on CI engine with emphasis to emissions—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 686-697.
    12. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    13. Kayadelen, Hasan Kayhan, 2018. "A multi-featured model for estimation of thermodynamic properties, adiabatic flame temperature and equilibrium combustion products of fuels, fuel blends, surrogates and fuel additives," Energy, Elsevier, vol. 143(C), pages 241-256.
    14. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    15. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    16. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    17. Raptotasios, Spiridon I. & Sakellaridis, Nikolaos F. & Papagiannakis, Roussos G. & Hountalas, Dimitrios T., 2015. "Application of a multi-zone combustion model to investigate the NOx reduction potential of two-stroke marine diesel engines using EGR," Applied Energy, Elsevier, vol. 157(C), pages 814-823.
    18. Taghavifar, Hamid & Mardani, Aref & Karim Maslak, Haleh, 2015. "A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 89(C), pages 358-364.
    19. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 2015. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR," Applied Energy, Elsevier, vol. 140(C), pages 52-64.
    20. Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1107-:d:502210. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.