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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
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    References listed on IDEAS

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    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.
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    Cited by:

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    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.
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    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. 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.
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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).
    17. 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.
    18. Raymond Kene & Thomas Olwal & Barend J. van Wyk, 2021. "Sustainable Electric Vehicle Transportation," Sustainability, MDPI, vol. 13(22), pages 1-16, November.

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