IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i16p12474-d1218696.html
   My bibliography  Save this article

Estimation of Fuel Consumption through PID Signals Using the Real Emissions Cycle in the City of Quito, Ecuador

Author

Listed:
  • Paúl Andrés Molina Campoverde

    (Grupo de Investigación en Ingeniería Del Transporte (GIIT), Universidad Politécnica Salesiana, Quito 170146, Ecuador)

Abstract

In Ecuador, according to data from the Ministry of Energy, the internal combustion engine is the largest consumer of fossil fuels. For this reason, it is important to identify and develop proposals in the literature that enable the prediction of vehicle fuel consumption in both the laboratory and on the road. To accomplish this, real driving emissions (RDEs) need to be contrasted against the development of an algorithm that characterizes forces that oppose such proposals. From experimental tests, fuel consumption information was collected through a flow meter connected to the fuel line and the engine’s characteristic curves were obtained through a chassis dynamometer. Then, from the parameter identification data (PID), the most important predictors were established through an ANOVA analysis. For the acquired variables, a neural network was implemented that could predict 99% of the estimates and present a relative error lower than 5% compared to common methods. Additionally, an algorithm was developed to calculate fuel consumption as a function of the gear, inertial forces, rolling resistance, slope, and aerodynamic force.

Suggested Citation

  • Paúl Andrés Molina Campoverde, 2023. "Estimation of Fuel Consumption through PID Signals Using the Real Emissions Cycle in the City of Quito, Ecuador," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12474-:d:1218696
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/16/12474/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/16/12474/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jakov Topić & Branimir Škugor & Joško Deur, 2022. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data," Sustainability, MDPI, vol. 14(2), pages 1-12, January.
    2. 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.
    3. Saerens, B. & Vandersteen, J. & Persoons, T. & Swevers, J. & Diehl, M. & Van den Bulck, E., 2009. "Minimization of the fuel consumption of a gasoline engine using dynamic optimization," Applied Energy, Elsevier, vol. 86(9), pages 1582-1588, September.
    4. Vicente Rojas-Reinoso & Janko Alvarez-Loor & Henrry Zambrano-Becerra & José Antonio Soriano, 2023. "Comparative Study of Gasoline Fuel Mixture to Reduce Emissions in the Metropolitan District," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    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. Jacek Pielecha & Karolina Kurtyka, 2023. "Exhaust Emissions from Euro 6 Vehicles in WLTC and RDE—Part 1: Methodology and Similarity Conditions Studies," Energies, MDPI, vol. 16(22), pages 1-27, November.

    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. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    2. Miretti, Federico & Misul, Daniela & Gennaro, Giulio & Ferrari, Antonio, 2022. "Hybridizing waterborne transport: Modeling and simulation of low-emissions hybrid waterbuses for the city of Venice," Energy, Elsevier, vol. 244(PB).
    3. Sergejus Lebedevas & Laurencas Raslavičius, 2021. "Prognostic Assessment of the Performance Parameters for the Industrial Diesel Engines Operated with Microalgae Oil," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    4. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    5. Mera, Zamir & Varella, Roberto & Baptista, Patrícia & Duarte, Gonçalo & Rosero, Fredy, 2022. "Including engine data for energy and pollutants assessment into the vehicle specific power methodology," Applied Energy, Elsevier, vol. 311(C).
    6. Zhu, Xinyi & Shen, Xiaoyan & Chen, Kailiang & Zhang, Zeqing, 2024. "Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM," Energy, Elsevier, vol. 296(C).
    7. Salvo, Orlando de & Vaz de Almeida, Flávio G., 2019. "Influence of technologies on energy efficiency results of official Brazilian tests of vehicle energy consumption," Applied Energy, Elsevier, vol. 241(C), pages 98-112.
    8. Frondel, Manuel & Marggraf, Clemens & Sommer, Stephan & Vance, Colin, 2021. "Reducing vehicle cold start emissions through carbon pricing: Evidence from Germany," Ruhr Economic Papers 896, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Hooftman, Nils & Messagie, Maarten & Van Mierlo, Joeri & Coosemans, Thierry, 2018. "A review of the European passenger car regulations – Real driving emissions vs local air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 1-21.
    10. Landry Frank Ineza Havugimana & Bolan Liu & Fanshuo Liu & Junwei Zhang & Ben Li & Peng Wan, 2023. "Review of Artificial Intelligent Algorithms for Engine Performance, Control, and Diagnosis," Energies, MDPI, vol. 16(3), pages 1-25, January.
    11. Carvalho, Irene & Baier, Thomas & Simoes, Ricardo & Silva, Arlindo, 2012. "Reducing fuel consumption through modular vehicle architectures," Applied Energy, Elsevier, vol. 93(C), pages 556-563.
    12. Evangelos G. Giakoumis & George Triantafillou, 2018. "Analysis of the Effect of Vehicle, Driving and Road Parameters on the Transient Performance and Emissions of a Turbocharged Truck," Energies, MDPI, vol. 11(2), pages 1-21, January.
    13. Evangelos G. Giakoumis & Alexandros T. Zachiotis, 2017. "Investigation of a Diesel-Engined Vehicle’s Performance and Emissions during the WLTC Driving Cycle—Comparison with the NEDC," Energies, MDPI, vol. 10(2), pages 1-19, February.
    14. Christian Farinango-Herrera & Joshebet Zambrano-Ramón & Edgar Vicente Rojas-Reinoso, 2024. "Thermographic Analysis of Exhaust Gas and Emissions by Varying Catalyst Behaviour and Injection Parameters," Energies, MDPI, vol. 17(6), pages 1-28, March.
    15. Fredy Rosero & Carlos Xavier Rosero & Carlos Segovia, 2024. "Towards Simpler Approaches for Assessing Fuel Efficiency and CO 2 Emissions of Vehicle Engines in Real Traffic Conditions Using On-Board Diagnostic Data," Energies, MDPI, vol. 17(19), pages 1-18, September.
    16. Achour, H. & Carton, J.G. & Olabi, A.G., 2011. "Estimating vehicle emissions from road transport, case study: Dublin City," Applied Energy, Elsevier, vol. 88(5), pages 1957-1964, May.
    17. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    18. Haoming Gu & Shenghua Liu & Yanju Wei & Xibin Liu & Xiaodong Zhu & Zheyang Li, 2022. "Effects of Polyoxymethylene Dimethyl Ethers Addition in Diesel on Real Driving Emission and Fuel Consumption Characteristics of a CHINA VI Heavy-Duty Vehicle," Energies, MDPI, vol. 15(7), pages 1-20, March.
    19. Salvi, B.L. & Subramanian, K.A., 2015. "Experimental investigation and phenomenological model development of flame kernel growth rate in a gasoline fuelled spark ignition engine," Applied Energy, Elsevier, vol. 139(C), pages 93-103.
    20. D'Errico, G. & Cerri, T. & Pertusi, G., 2011. "Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models," Applied Energy, Elsevier, vol. 88(3), pages 767-777, March.

    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:jsusta:v:15:y:2023:i:16:p:12474-:d:1218696. 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.