IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2021i1p82-d709278.html
   My bibliography  Save this article

Traffic and Energy Consumption Modelling of Electric Vehicles: Parameter Updating from Floating and Probe Vehicle Data

Author

Listed:
  • Antonello Ignazio Croce

    (Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy)

  • Giuseppe Musolino

    (Dipartimento di ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università degli Studi Mediterranea di Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy)

  • Corrado Rindone

    (Dipartimento di ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università degli Studi Mediterranea di Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy)

  • Antonino Vitetta

    (Dipartimento di ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Università degli Studi Mediterranea di Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy)

Abstract

This paper focuses on the estimation of energy consumption of Electric Vehicles (EVs) by means of models derived from traffic flow theory and vehicle locomotion laws. In particular, it proposes a bi-level procedure with the aim to calibrate (or update) the whole parameters of traffic flow models and energy consumption laws by means of Floating Car Data (FCD) and probe vehicle data. The reported models may be part of a procedure for designing and planning transport and energy systems. This aim is to verify if, and in what amount, the existing parameters of the resistances/energy consumptions model calibrated in the literature for Internal Combustion Engines Vehicles (ICEVs) change for EVs, considering the above circular dependency between supply, demand, and supply–demand interaction. The final results concern updated parameters to be used for eco-driving and eco-routing applications for design and a planning transport system adopting a multidisciplinary approach. The focus of this manuscript is on the transport area. Experimental data concern vehicular data extracted from traffic (floating car data and probe vehicle data) and energy consumption data measured for equipped EVs performing trips inside a sub-regional area, located in the Città Metropolitana of Reggio Calabria (Italy). The results of the calibration process are encouraging, as they allow for updating parameters related to energy consumption and energy recovered in terms of EVs obtained from data observed in real conditions. The latter term is relevant in EVs, particularly on urban routes where drivers experience unstable traffic conditions.

Suggested Citation

  • Antonello Ignazio Croce & Giuseppe Musolino & Corrado Rindone & Antonino Vitetta, 2021. "Traffic and Energy Consumption Modelling of Electric Vehicles: Parameter Updating from Floating and Probe Vehicle Data," Energies, MDPI, vol. 15(1), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:82-:d:709278
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/1/82/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/1/82/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ennio Cascetta, 2009. "Transportation Systems Analysis," Springer Optimization and Its Applications, Springer, number 978-0-387-75857-2, September.
    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. Hamza Mediouni & Amal Ezzouhri & Zakaria Charouh & Khadija El Harouri & Soumia El Hani & Mounir Ghogho, 2022. "Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach," Energies, MDPI, vol. 15(17), pages 1-17, 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. Paola Panuccio, 2019. "Smart Planning: From City to Territorial System," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
    2. Pierluigi Coppola & Fulvio Silvestri, 2021. "Gender Inequality in Safety and Security Perceptions in Railway Stations," Sustainability, MDPI, vol. 13(7), pages 1-15, April.
    3. Federico Benassi & Marica D'Elia & Francesca Petrei, 2021. "The “meso” dimension of territorial capital: Evidence from Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 159-175, February.
    4. Vitalii Naumov & Andrzej Szarata & Hanna Vasiutina, 2022. "Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland," Energies, MDPI, vol. 15(14), pages 1-23, July.
    5. Igor Lazov, 2019. "A Methodology for Revenue Analysis of Parking Lots," Networks and Spatial Economics, Springer, vol. 19(1), pages 177-198, March.
    6. Harshad Khadilkar, 2017. "Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks," Transportation Science, INFORMS, vol. 51(4), pages 1161-1176, November.
    7. Wang, Xinchang & Meng, Qiang & Miao, Lixin, 2016. "Delimiting port hinterlands based on intermodal network flows: Model and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 32-51.
    8. Reis, Vasco, 2014. "Analysis of mode choice variables in short-distance intermodal freight transport using an agent-based model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 100-120.
    9. Lin, Ting (Grace) & Xia, Jianhong (Cecilia) & Robinson, Todd P. & Goulias, Konstadinos G. & Church, Richard L. & Olaru, Doina & Tapin, John & Han, Renlong, 2014. "Spatial analysis of access to and accessibility surrounding train stations: a case study of accessibility for the elderly in Perth, Western Australia," Journal of Transport Geography, Elsevier, vol. 39(C), pages 111-120.
    10. Chakraborty, Rahul & Chakravarty, Sujoy, 2023. "Factors affecting acceptance of electric two-wheelers in India: A discrete choice survey," Transport Policy, Elsevier, vol. 132(C), pages 27-41.
    11. Cordera, Rubén & Sañudo, Roberto & dell’Olio, Luigi & Ibeas, Ángel, 2018. "Trip distribution model for regional railway services considering spatial effects between stations," Transport Policy, Elsevier, vol. 67(C), pages 77-84.
    12. Mancuso, Paolo, 2014. "An analysis of the competition that impinges on the Milan–Rome intercity passenger transport link," Transport Policy, Elsevier, vol. 32(C), pages 42-52.
    13. Paolo Delle Site, 2017. "On the Equivalence Between SUE and Fixed-Point States of Day-to-Day Assignment Processes with Serially-Correlated Route Choice," Networks and Spatial Economics, Springer, vol. 17(3), pages 935-962, September.
    14. Francis Oremo & Richard Mulwa & Nicholas Oguge, 2021. "Sustainable water access and willingness of smallholder irrigators to pay for on-farm water storage systems in Tsavo sub-catchment, Kenya," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 1371-1391, February.
    15. Croce, Antonello Ignazio & Musolino, Giuseppe & Rindone, Corrado & Vitetta, Antonino, 2019. "Sustainable mobility and energy resources: A quantitative assessment of transport services with electrical vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    16. Wekesa, Bright Masakha, 2017. "Effect Of Climate Smart Agricultural Practices On Food Security Of Small Scale Farmers In Teso North Sub-County, Kenya," Research Theses 276427, Collaborative Masters Program in Agricultural and Applied Economics.
    17. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    18. Cartenì, Armando & Marzano, Vittorio & Henke, Ilaria & Cascetta, Ennio, 2022. "A cognitive and participative decision-making model for transportation planning under different uncertainty levels," Transport Policy, Elsevier, vol. 116(C), pages 386-398.
    19. Jacek Zak & Miroslaw Kruszynski, 2021. "Comprehensive, Multiple Level Assessment and Multiple Criteria Ranking of Transportation Projects," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 5), pages 506-532.
    20. Justen, Andreas & Schippl, Jens & Lenz, Barbara & Fleischer, Torsten, 2014. "Assessment of policies and detection of unintended effects: Guiding principles for the consideration of methods and tools in policy-packaging," Transportation Research Part A: Policy and Practice, Elsevier, vol. 60(C), pages 19-30.

    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:jeners:v:15:y:2021:i:1:p:82-:d:709278. 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.