IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v7y2015i10p14326-14343d57585.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/7/10/14326/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/7/10/14326/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    7. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.

    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. Jaller, Miguel & Pahwa, Anmol & Zhang, Michael, 2021. "Cargo Routing and Disadvantaged Communities," Institute of Transportation Studies, Working Paper Series qt9qg2318x, Institute of Transportation Studies, UC Davis.
    2. Cailou Jiang & Ying Zhang & Maoliang Bu & Weishu Liu, 2018. "The Effectiveness of Government Subsidies on Manufacturing Innovation: Evidence from the New Energy Vehicle Industry in China," Sustainability, MDPI, vol. 10(6), pages 1-11, May.
    3. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    4. Guoqiang Zhang & Yanmei Xu & Juan Zhang, 2016. "Consumer-Oriented Policy towards Diffusion of Electric Vehicles: City-Level Evidence from China," Sustainability, MDPI, vol. 8(12), pages 1-16, December.
    5. Thanh Tung Ha & Thanh Chuong Nguyen & Sy Sua Tu & Minh Hieu Nguyen, 2023. "Investigation of Influential Factors of Intention to Adopt Electric Vehicles for Motorcyclists in Vietnam," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    6. Yang Wang & Alessandra Boggio-Marzet, 2018. "Evaluation of Eco-Driving Training for Fuel Efficiency and Emissions Reduction According to Road Type," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    7. Changhong Deng & Ning Liang & Jin Tan & Gongchen Wang, 2016. "Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network," Sustainability, MDPI, vol. 8(12), pages 1-15, November.
    8. Wojciech Adamski & Krzysztof Brzozowski & Jacek Nowakowski & Tomasz Praszkiewicz & Tomasz Knefel, 2021. "Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions," Energies, MDPI, vol. 14(23), pages 1-15, November.
    9. Alejandro G. Tuero & Laura Pozueco & Roberto García & Gabriel Díaz & Xabiel G. Pañeda & David Melendi & Abel Rionda & David Martínez, 2017. "Economic Impact of the Use of Inertia in an Urban Bus Company," Energies, MDPI, vol. 10(7), pages 1-17, July.
    10. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    11. Juan Francisco Coloma & Marta García & Gonzalo Fernández & Andrés Monzón, 2021. "Environmental Effects of Eco-Driving on Courier Delivery," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    12. Cui, Lianbiao & Li, Rongjing & Song, Malin & Zhu, Lei, 2019. "Can China achieve its 2030 energy development targets by fulfilling carbon intensity reduction commitments?," Energy Economics, Elsevier, vol. 83(C), pages 61-73.
    13. Wesseling , Joeri H., 2015. "Explaining differences in electric vehicle policies across countries: innovation vs. environmental policy rationale," Papers in Innovation Studies 2015/42, Lund University, CIRCLE - Centre for Innovation Research.
    14. Ajanovic, Amela & Haas, Reinhard, 2016. "Dissemination of electric vehicles in urban areas: Major factors for success," Energy, Elsevier, vol. 115(P2), pages 1451-1458.
    15. Elena Higueras-Castillo & Sebastian Molinillo & J. Andres Coca-Stefaniak & Francisco Liébana-Cabanillas, 2019. "Perceived Value and Customer Adoption of Electric and Hybrid Vehicles," Sustainability, MDPI, vol. 11(18), pages 1-15, September.
    16. Anders F. Jensen & Thomas K. Rasmussen & Carlo G. Prato, 2020. "A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    17. Wojciech Lewicki & Wojciech Drozdz & Piotr Wroblewski & Krzysztof Zarna, 2021. "The Road to Electromobility in Poland: Consumer Attitude Assessment," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 28-39.
    18. Guan Wang & Jintao Lai & Zhexi Lian & Zhen Zhang, 2023. "An Eco-Driving Strategy Considering Phase-Switch-Based Bus Lane Sharing," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    19. Zhang, Junjie & Jia, Rongwen & Yang, Hangjun & Dong, Kangyin, 2022. "Does electric vehicle promotion in the public sector contribute to urban transport carbon emissions reduction?," Transport Policy, Elsevier, vol. 125(C), pages 151-163.
    20. Strömberg, Helena & Karlsson, I.C. MariAnne & Rexfelt, Oskar, 2015. "Eco-driving: Drivers’ understanding of the concept and implications for future interventions," Transport Policy, Elsevier, vol. 39(C), pages 48-54.

    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:7:y:2015:i:10:p:14326-14343:d:57585. 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.