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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anatole Desreveaux & Alain Bouscayrol & Elodie Castex & Rochdi Trigui & Eric Hittinger & Gabriel-Mihai Sirbu, 2020. "Annual Variation in Energy Consumption of an Electric Vehicle Used for Commuting," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. Alexander Wahl & Christoph Wellmann & Björn Krautwig & Patrick Manns & Bicheng Chen & Christof Schernus & Jakob Andert, 2022. "Efficiency Increase through Model Predictive Thermal Control of Electric Vehicle Powertrains," Energies, MDPI, vol. 15(4), pages 1-21, February.
    3. Martin Koreny & Petr Simonik & Tomas Klein & Tomas Mrovec & Joy Jason Ligori, 2022. "Hybrid Research Platform for Fundamental and Empirical Modeling and Analysis of Energy Management of Shared Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-25, February.
    4. Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2020. "A Method for the Optimization of Daily Activity Chains Including Electric Vehicles," Energies, MDPI, vol. 13(4), pages 1-21, February.
    5. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    6. Bogdan Ovidiu Varga & Arsen Sagoian & Florin Mariasiu, 2019. "Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges," Energies, MDPI, vol. 12(5), pages 1-19, March.
    7. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    8. 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).
    9. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    10. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    11. Hatem Abdelaty & Moataz Mohamed, 2021. "A Prediction Model for Battery Electric Bus Energy Consumption in Transit," Energies, MDPI, vol. 14(10), pages 1-26, May.
    12. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    13. Ma, Xiaolei & Miao, Ran & Wu, Xinkai & Liu, Xianglong, 2021. "Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing," Energy, Elsevier, vol. 216(C).
    14. Adriana Skuza & Emilia M. Szumska & Rafał Jurecki & Artur Pawelec, 2024. "Modeling the Impact of Traffic Parameters on Electric Vehicle Energy Consumption," Energies, MDPI, vol. 17(21), pages 1-19, October.
    15. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
    16. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    17. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    18. Fabio Vacca & Stefano De Pinto & Ahu Ece Hartavi Karci & Patrick Gruber & Fabio Viotto & Carlo Cavallino & Jacopo Rossi & Aldo Sorniotti, 2017. "On the Energy Efficiency of Dual Clutch Transmissions and Automated Manual Transmissions," Energies, MDPI, vol. 10(10), pages 1-22, October.
    19. Hariharan, C. & Gunadevan, D. & Arun Prakash, S. & Latha, K. & Antony Aroul Raj, V. & Velraj, R., 2022. "Simulation of battery energy consumption in an electric car with traction and HVAC model for a given source and destination for reducing the range anxiety of the driver," Energy, Elsevier, vol. 249(C).
    20. 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.
    21. Raymond Kene & Thomas Olwal & Barend J. van Wyk, 2021. "Sustainable Electric Vehicle Transportation," Sustainability, MDPI, vol. 13(22), pages 1-16, November.

    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.