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

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  • 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
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    References listed on IDEAS

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    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.
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