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Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence

Author

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  • Sasanka Katreddi

    (Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA)

  • Arvind Thiruvengadam

    (Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26505, USA)

Abstract

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared ( R 2 ) and lower root mean square error.

Suggested Citation

  • Sasanka Katreddi & Arvind Thiruvengadam, 2021. "Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence," Energies, MDPI, vol. 14(24), pages 1-12, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8592-:d:706997
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    References listed on IDEAS

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    1. Soren T. Anderson & Ian W. H. Parry & James M. Sallee & Carolyn Fischer, 2011. "Automobile Fuel Economy Standards: Impacts, Efficiency, and Alternatives," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 5(1), pages 89-108, Winter.
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    4. Muhammad Shahid Hassan & Haider Mahmood & Muhammad Naveed Tahir & Tarek Tawfik Yousef Alkhateeb & Ayesha Wajid & Dimitri Volchenkov, 2021. "Governance: A Source to Increase Tax Revenue in Pakistan," Complexity, Hindawi, vol. 2021, pages 1-11, April.
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    Cited by:

    1. 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.
    2. Dengfeng Zhao & Haiyang Li & Junjian Hou & Pengliang Gong & Yudong Zhong & Wenbin He & Zhijun Fu, 2023. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption," Energies, MDPI, vol. 16(14), pages 1-20, July.
    3. Seongin Jo & Hyung Jun Kim & Sang Il Kwon & Jong Tae Lee & Suhan Park, 2023. "Assessment of Energy Consumption Characteristics of Ultra-Heavy-Duty Vehicles under Real Driving Conditions," Energies, MDPI, vol. 16(5), pages 1-18, February.

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