IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i5p608-d97323.html
   My bibliography  Save this article

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Author

Listed:
  • Cedric De Cauwer

    (Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Wouter Verbeke

    (Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Thierry Coosemans

    (Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Saphir Faid

    (Punch Powertrain, Industriezone Schurhovenveld 4125, 3800 Sint-Truiden, Belgium)

  • Joeri Van Mierlo

    (Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

Abstract

Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error ( MAE ) of 12–14% of the average trip consumption, of which 7–9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.

Suggested Citation

  • Cedric De Cauwer & Wouter Verbeke & Thierry Coosemans & Saphir Faid & Joeri Van Mierlo, 2017. "A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions," Energies, MDPI, vol. 10(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:608-:d:97323
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/5/608/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/5/608/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maarten Messagie & Faycal-Siddikou Boureima & Thierry Coosemans & Cathy Macharis & Joeri Van Mierlo, 2014. "A Range-Based Vehicle Life Cycle Assessment Incorporating Variability in the Environmental Assessment of Different Vehicle Technologies and Fuels," Energies, MDPI, vol. 7(3), pages 1-16, March.
    Full references (including those not matched with items on IDEAS)

    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. Cai, Yanpeng & Applegate, Scott & Yue, Wencong & Cai, Jianying & Wang, Xuan & Liu, Gengyuan & Li, Chunhui, 2017. "A hybrid life cycle and multi-criteria decision analysis approach for identifying sustainable development strategies of Beijing's taxi fleet," Energy Policy, Elsevier, vol. 100(C), pages 314-325.
    2. Chun Yang & Jui-Che Tu & Qianling Jiang, 2020. "The Influential Factors of Consumers’ Sustainable Consumption: A Case on Electric Vehicles in China," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    3. Marmiroli, Benedetta & Venditti, Mattia & Dotelli, Giovanni & Spessa, Ezio, 2020. "The transport of goods in the urban environment: A comparative life cycle assessment of electric, compressed natural gas and diesel light-duty vehicles," Applied Energy, Elsevier, vol. 260(C).
    4. Anca N. Iuga (Butnariu) & Vasile N. Popa & Luminița I. Popa, 2018. "Comparative Analysis of Automotive Products Regarding the Influence of Eco-Friendly Methods to Emissions’ Reduction," Energies, MDPI, vol. 12(1), pages 1-24, December.
    5. Hasan-Basri, Bakti & Mohd Mustafa, Muzafarshah & Bakar, Normizan, 2019. "Are Malaysian Consumers Willing to Pay for Hybrid Cars’ Attributes?," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 53(1), pages 121-134.
    6. Cox, Brian & Bauer, Christian & Mendoza Beltran, Angelica & van Vuuren, Detlef P. & Mutel, Christopher L., 2020. "Life cycle environmental and cost comparison of current and future passenger cars under different energy scenarios," Applied Energy, Elsevier, vol. 269(C).
    7. Felipe Cerdas & Paul Titscher & Nicolas Bognar & Richard Schmuch & Martin Winter & Arno Kwade & Christoph Herrmann, 2018. "Exploring the Effect of Increased Energy Density on the Environmental Impacts of Traction Batteries: A Comparison of Energy Optimized Lithium-Ion and Lithium-Sulfur Batteries for Mobility Applications," Energies, MDPI, vol. 11(1), pages 1-20, January.
    8. Mayank Jha & Frede Blaabjerg & Mohammed Ali Khan & Varaha Satya Bharath Kurukuru & Ahteshamul Haque, 2019. "Intelligent Control of Converter for Electric Vehicles Charging Station," Energies, MDPI, vol. 12(12), pages 1-25, June.
    9. Xin Sun & Vanessa Bach & Matthias Finkbeiner & Jianxin Yang, 2021. "Criticality Assessment of the Life Cycle of Passenger Vehicles Produced in China," Circular Economy and Sustainability, Springer, vol. 1(1), pages 435-455, June.
    10. Siqin Xiong & Junping Ji & Xiaoming Ma, 2019. "Comparative Life Cycle Energy and GHG Emission Analysis for BEVs and PhEVs: A Case Study in China," Energies, MDPI, vol. 12(5), pages 1-17, March.
    11. Danielis, Romeo & Giansoldati, Marco & Scorrano, Mariangela, 2019. "Comparing the life-cycle CO2 emissions of the best-selling electric and internal combustion engine cars in Italy," Working Papers 19_1, SIET Società Italiana di Economia dei Trasporti e della Logistica.
    12. David Borge-Diez & Pedro Miguel Ortega-Cabezas & Antonio Colmenar-Santos & Jorge Juan Blanes-Peiró, 2021. "Contribution of Driving Efficiency to Vehicle-to-Building," Energies, MDPI, vol. 14(12), pages 1-30, June.
    13. Eckard Helmers & Johannes Dietz & Martin Weiss, 2020. "Sensitivity Analysis in the Life-Cycle Assessment of Electric vs. Combustion Engine Cars under Approximate Real-World Conditions," Sustainability, MDPI, vol. 12(3), pages 1-31, February.
    14. Lei Zhang & Zhenpo Wang & Fengchun Sun & David G. Dorrell, 2014. "Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter," Energies, MDPI, vol. 7(5), pages 1-14, May.
    15. Hooftman, Nils & Messagie, Maarten & Van Mierlo, Joeri & Coosemans, Thierry, 2018. "A review of the European passenger car regulations – Real driving emissions vs local air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 1-21.
    16. Wojciech Cieslik & Filip Szwajca & Wojciech Golimowski & Andrew Berger, 2021. "Experimental Analysis of Residential Photovoltaic (PV) and Electric Vehicle (EV) Systems in Terms of Annual Energy Utilization," Energies, MDPI, vol. 14(4), pages 1-21, February.
    17. Gautham Ram Chandra Mouli & Peter Van Duijsen & Francesca Grazian & Ajay Jamodkar & Pavol Bauer & Olindo Isabella, 2020. "Sustainable E-Bike Charging Station That Enables AC, DC and Wireless Charging from Solar Energy," Energies, MDPI, vol. 13(14), pages 1-21, July.
    18. Sergio Maria Patella & Flavio Scrucca & Francesco Asdrubali & Stefano Carrese, 2019. "Traffic Simulation-Based Approach for A Cradle-to-Grave Greenhouse Gases Emission Model," Sustainability, MDPI, vol. 11(16), pages 1-14, August.
    19. Shafique, Muhammad & Azam, Anam & Rafiq, Muhammad & Luo, Xiaowei, 2022. "Life cycle assessment of electric vehicles and internal combustion engine vehicles: A case study of Hong Kong," Research in Transportation Economics, Elsevier, vol. 91(C).
    20. Bauer, Christian & Hofer, Johannes & Althaus, Hans-Jörg & Del Duce, Andrea & Simons, Andrew, 2015. "The environmental performance of current and future passenger vehicles: Life cycle assessment based on a novel scenario analysis framework," Applied Energy, Elsevier, vol. 157(C), pages 871-883.

    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:10:y:2017:i:5:p:608-:d:97323. 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.