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A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems

Author

Listed:
  • Gennaro Nicola Bifulco

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, via Claudio 21, 80125 Naples, Italy)

  • Francesco Galante

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, via Claudio 21, 80125 Naples, Italy)

  • Luigi Pariota

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, via Claudio 21, 80125 Naples, Italy)

  • Maria Russo Spena

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, via Claudio 21, 80125 Naples, Italy)

Abstract

Reduction of the environmental impact of cars represents one of the biggest transport industry challenges. Beyond more efficient engines, a promising approach is to use eco-driving technologies that help drivers achieve lower fuel consumption and emission levels. In this study, a real-time microscopic fuel consumption model was developed. It was designed to be integrated into simulation platforms for the design and testing of Advanced Driving Assistance Systems (ADAS), aimed at keeping the vehicle within the environmentally friendly driving zone and hence reducing harmful exhaust gases. To allow integration in platforms employed at early stages of ADAS development and testing, the model was kept very simple and dependent on a few easily computable variables. To show the feasibility of the identification of the model (and to validate it), a large experiment involving more than 100 drivers and about 8000 km of driving was carried out using an instrumented vehicle. An instantaneous model was identified based on vehicle speed, acceleration level and gas pedal excursion, applicable in an extra-urban traffic context. Both instantaneous and aggregate validation was performed and the model was shown to estimate vehicle fuel consumption consistently with in-field instantaneous measurements. Very accurate estimations were also shown for the aggregate consumption of each driving session.

Suggested Citation

  • Gennaro Nicola Bifulco & Francesco Galante & Luigi Pariota & Maria Russo Spena, 2015. "A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems," Sustainability, MDPI, vol. 7(10), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:10:p:14326-14343:d:57585
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    References listed on IDEAS

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    1. Xingping Zhang & Jian Xie & Rao Rao & Yanni Liang, 2014. "Policy Incentives for the Adoption of Electric Vehicles across Countries," Sustainability, MDPI, vol. 6(11), pages 1-23, November.
    2. Akcelik, Rahmi, 1989. "Efficiency and drag in the power-based model of fuel consumption," Transportation Research Part B: Methodological, Elsevier, vol. 23(5), pages 376-385, October.
    3. Yuanying Chi & Zhengquan Guo & Yuhua Zheng & Xingping Zhang, 2014. "Scenarios Analysis of the Energies’ Consumption and Carbon Emissions in China Based on a Dynamic CGE Model," Sustainability, MDPI, vol. 6(2), pages 1-26, January.
    4. Sivak, Michael & Schoettle, Brandon, 2012. "Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy," Transport Policy, Elsevier, vol. 22(C), pages 96-99.
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    Cited by:

    1. Qian Cheng & Xiaobei Jiang & Haodong Zhang & Wuhong Wang & Chunwen Sun, 2020. "Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    2. Juan Francisco Coloma & Marta García & Yang Wang & Andrés Monzón, 2017. "Green Eco-Driving Effects in Non-Congested Cities," Sustainability, MDPI, vol. 10(1), pages 1-16, December.
    3. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    4. Aleš Hace, 2019. "The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft," Energies, MDPI, vol. 12(15), pages 1-31, August.
    5. Ehsan Moradi & Luis Miranda-Moreno, 2022. "A Mixed Ensemble Learning and Time-Series Methodology for Category-Specific Vehicular Energy and Emissions Modeling," Sustainability, MDPI, vol. 14(3), pages 1-26, February.
    6. 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.
    7. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.

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