IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2021i1p82-d709278.html

Some searches may not work properly. We apologize for the inconvenience.

   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, October.
    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. Kepaptsoglou, Konstantinos & Stathopoulos, Antony & Karlaftis, Matthew G., 2017. "Ridership estimation of a new LRT system: Direct demand model approach," Journal of Transport Geography, Elsevier, vol. 58(C), pages 146-156.
    3. Piyapong Suwanno & Chaiwat Yaibok & Noriyasu Tsumita & Atsushi Fukuda & Kestsirin Theerathitichaipa & Manlika Seefong & Sajjakaj Jomnonkwao & Rattanaporn Kasemsri, 2023. "Estimation of the Evacuation Time According to Different Flood Depths," Sustainability, MDPI, vol. 15(7), pages 1-23, April.
    4. Pierluigi Coppola & Fulvio Silvestri, 2021. "Gender Inequality in Safety and Security Perceptions in Railway Stations," Sustainability, MDPI, vol. 13(7), pages 1-15, April.
    5. Helai Huang & Jialing Wu & Fang Liu & Yiwei Wang, 2020. "Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
    6. David Watling & Giulio Cantarella, 2015. "Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems," Networks and Spatial Economics, Springer, vol. 15(3), pages 843-882, September.
    7. Rinaldi, Marco & Viti, Francesco, 2017. "Exact and approximate route set generation for resilient partial observability in sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 86-119.
    8. Luís M. Fernandes & Joaquim J. Júdice & Hanif D. Sherali & António P. Antunes, 2011. "Siting and Sizing of Facilities under Probabilistic Demands," Journal of Optimization Theory and Applications, Springer, vol. 149(2), pages 420-440, May.
    9. Eva Malichová & Ghadir Pourhashem & Tatiana Kováčiková & Martin Hudák, 2020. "Users’ Perception of Value of Travel Time and Value of Ridesharing Impacts on Europeans’ Ridesharing Participation Intention: A Case Study Based on MoTiV European-Wide Mobility and Behavioral Pattern ," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    10. 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.
    11. 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.
    12. Juying Wang & Feng Guan & Ting Li & Can Wang & Qianqian Han & Bin Yu, 2015. "Optimization of the Waterbus Operation Plan Considering Carbon Emissions: The Case of Zhoushan City," Sustainability, MDPI, vol. 7(8), pages 1-18, August.
    13. Igor Lazov, 2019. "A Methodology for Revenue Analysis of Parking Lots," Networks and Spatial Economics, Springer, vol. 19(1), pages 177-198, March.
    14. Harshad Khadilkar, 2017. "Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks," Transportation Science, INFORMS, vol. 51(4), pages 1161-1176, November.
    15. 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.
    16. A. Baral & S. M. Shahandashti, 2022. "Identifying critical combination of roadside slopes susceptible to rainfall-induced failures," 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. 113(2), pages 1177-1198, September.
    17. Caggiani, Leonardo & Ottomanelli, Michele & Dell’Orco, Mauro, 2014. "Handling uncertainty in Multi Regional Input-Output models by entropy maximization and fuzzy programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 159-172.
    18. 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.
    19. 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.
    20. 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.

    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.