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Electric vehicle routing problem with machine learning for energy prediction

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  • Basso, Rafael
  • Kulcsár, Balázs
  • Sanchez-Diaz, Ivan

Abstract

Routing electric commercial vehicles requires taking into account their limited driving range, which is affected by several uncertain factors such as traffic conditions. This paper presents the time-dependent Electric Vehicle Routing Problem with Chance-Constraints (EVRP-CC) and partial recharging. The routing method is divided into two stages, where the first finds the best paths and the second optimizes the routes. A probabilistic Bayesian machine learning approach is proposed for predicting the expected energy consumption and variance for the road links, paths and routes. Hence it is possible to consider the uncertainty in energy demand by planning charging within a confidence interval. The energy estimation is validated with data from electric buses driving a public transport route in Gothenburg-Sweden as well as with realistic simulations for 24 hours traffic in the city of Luxembourg connected to a high fidelity vehicle model. Routing solutions are compared with a deterministic formulation of the problem similar to the ones found in the literature. The results indicate high accuracy for the energy prediction as well as energy savings and more reliability for the routes.

Suggested Citation

  • Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan, 2021. "Electric vehicle routing problem with machine learning for energy prediction," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 24-55.
    • Handle: RePEc:eee:transb:v:145:y:2021:i:c:p:24-55
    DOI: 10.1016/j.trb.2020.12.007
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    Citations

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    Cited by:

    1. Witsarut Achariyaviriya & Wongkot Wongsapai & Kittitat Janpoom & Tossapon Katongtung & Yuttana Mona & Nakorn Tippayawong & Pana Suttakul, 2023. "Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches," Energies, MDPI, vol. 16(17), pages 1-14, September.
    2. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-12, September.
    3. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    4. Wang, Ruiting & Keyantuo, Patrick & Zeng, Teng & Sandoval, Jairo & Vishwanath, Aashrith & Borhan, Hoseinali & Moura, Scott, 2024. "Robust routing for a mixed fleet of heavy-duty trucks with pickup and delivery under energy consumption uncertainty," Applied Energy, Elsevier, vol. 368(C).
    5. Stanisław Iwan & Mariusz Nürnberg & Artur Bejger & Kinga Kijewska & Krzysztof Małecki, 2021. "Unloading Bays as Charging Stations for EFV-Based Urban Freight Delivery System—Example of Szczecin," Energies, MDPI, vol. 14(18), pages 1-22, September.
    6. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    7. Roman Michael Sennefelder & Rubén Martín-Clemente & Ramón González-Carvajal, 2023. "Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression," Energies, MDPI, vol. 16(11), pages 1-14, May.
    8. Weimin Ma & Jiakai Chen & Hua Ke, 2021. "Electric Vehicle Assignment Considering Users’ Waiting Time," Sustainability, MDPI, vol. 13(23), pages 1-14, December.
    9. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    10. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    11. 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.
    12. 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).
    13. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    14. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

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