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. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Ajanovic, Amela & Haas, Reinhard, 2016. "Dissemination of electric vehicles in urban areas: Major factors for success," Energy, Elsevier, vol. 115(P2), pages 1451-1458.
    9. 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.
    10. 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.
    11. 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.
    12. Yecid Alfonso Mu oz Maldonado & C sar Acevedo & Edward Jerez & Carlos Sarmiento & Miguel De La Rosa & Adalberto Ospino, 2021. "Transition of Electric Mobility in Colombia: Technical and Economic Evaluation of Scenarios for the Integration of E-taxis in Bucaramanga," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 461-469.
    13. Dingzhong Feng & Lei Ma & Yangke Ding & Guanghua Wu & Ye Zhang, 2019. "Decisions of the Dual-Channel Supply Chain under Double Policy Considering Remanufacturing," IJERPH, MDPI, vol. 16(3), pages 1-20, February.
    14. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    15. Wenbo Li & Ruyin Long & Hong Chen & Baoqi Dou & Feiyu Chen & Xiao Zheng & Zhengxia He, 2020. "Public Preference for Electric Vehicle Incentive Policies in China: A Conjoint Analysis," IJERPH, MDPI, vol. 17(1), pages 1-16, January.
    16. Liu, Xiaoling & Sun, Xiaohua & Zheng, Hui & Huang, Dongdong, 2021. "Do policy incentives drive electric vehicle adoption? Evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 49-62.
    17. Piotr Rosik & Sławomir Goliszek & Tomasz Komornicki & Patryk Duma, 2021. "Forecast of the Impact of Electric Car Battery Performance and Infrastructural and Demographic Changes on Cumulative Accessibility for the Five Most Populous Cities in Poland," Energies, MDPI, vol. 14(24), pages 1-12, December.
    18. Alali, Layla & Niesten, Eva & Gagliardi, Dimitri, 2022. "The impact of UK financial incentives on the adoption of electric fleets: The moderation effect of GDP change," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 200-220.
    19. Wenwen Li & Wenping Wang & Yu Wang & Yingbo Qin, 2017. "Industrial structure, technological progress and CO2 emissions in China: Analysis based on the STIRPAT framework," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1545-1564, September.
    20. Kwon, Yeongmin & Son, Sanghoon & Jang, Kitae, 2018. "Evaluation of incentive policies for electric vehicles: An experimental study on Jeju Island," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 404-412.

    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.