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Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Author

Listed:
  • Magdalena Rykała

    (Faculty of Security, Logistics and Management, Military University of Technology, 00-908 Warsaw, Poland)

  • Małgorzata Grzelak

    (Faculty of Security, Logistics and Management, Military University of Technology, 00-908 Warsaw, Poland)

  • Łukasz Rykała

    (Faculty of Mechanical Engineering, Military University of Technology, 00-908 Warsaw, Poland)

  • Daniela Voicu

    (Faculty of Aircraft and Military Vehicles, Military Technical Academy “Ferdinand I”, 050141 Bucharest, Romania)

  • Ramona-Monica Stoica

    (Faculty of Aircraft and Military Vehicles, Military Technical Academy “Ferdinand I”, 050141 Bucharest, Romania)

Abstract

As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

Suggested Citation

  • Magdalena Rykała & Małgorzata Grzelak & Łukasz Rykała & Daniela Voicu & Ramona-Monica Stoica, 2023. "Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface," Energies, MDPI, vol. 16(21), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7266-:d:1267793
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    References listed on IDEAS

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    1. Jiangtao Sun & Wei Dang & Fengqin Wang & Haikuan Nie & Xiaoliang Wei & Pei Li & Shaohua Zhang & Yubo Feng & Fei Li, 2023. "Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost," Energies, MDPI, vol. 16(10), pages 1-26, May.
    2. Roman Michael Sennefelder & Rubén Martín-Clemente & Ramón González-Carvajal, 2023. "Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression," Energies, MDPI, vol. 16(11), pages 1-14, May.
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