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Data-driven, personalized route planning for connected electric vehicles: Optimizing time, energy, and charging stops

Author

Listed:
  • Houalef, Ahmed-Ramzi
  • Delavernhe, Florian
  • Senouci, Sidi-Mohammed
  • Aglzim, El-Hassane

Abstract

Electric vehicles (EVs) present a sustainable alternative to traditional vehicles, yet challenges like range anxiety caused by limited driving range and inaccurate energy predictions hinder widespread adoption. This study introduces a solution using connected electric vehicles (CEVs) equipped with sensors and onboard computers for real-time data collection. The data support a personalized energy model that adapts to individual driving behaviors and environmental conditions, ensuring accurate energy consumption predictions and alleviating range anxiety. A comprehensive framework for time- and energy-optimal route planning is proposed, featuring intelligent charging station (CS) recommendations. This framework integrates factors such as ambient temperature, traffic density, road gradients, and driver-specific speed profiles to predict energy requirements for both traction and auxiliary systems. Charging station recommendations are dynamically optimized using a bidirectional search within an adapted Bellman-Ford algorithm. This search balances energy and time efficiency by evaluating energy constraints across the route, from the source to intermediate charging stations and the final destination. The method provides both energy-optimal and sub-optimal paths that consider real-time CS availability. Simulation tests on routes from Paris to surrounding cities demonstrate up to 25 % energy efficiency improvement over traditional time- or distance-optimized routes. The energy model achieves a high accuracy, with a 2.66 % error margin for State of Charge (SoC) and 0.6 kWh for energy consumption. By combining advanced predictive capabilities, real-time optimization, and dynamic CS recommendations, this research addresses critical EV adoption barriers and promotes environmental sustainability.

Suggested Citation

  • Houalef, Ahmed-Ramzi & Delavernhe, Florian & Senouci, Sidi-Mohammed & Aglzim, El-Hassane, 2025. "Data-driven, personalized route planning for connected electric vehicles: Optimizing time, energy, and charging stops," Applied Energy, Elsevier, vol. 402(PA).
    • Handle: RePEc:eee:appene:v:402:y:2025:i:pa:s0306261925016174
    DOI: 10.1016/j.apenergy.2025.126887
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    References listed on IDEAS

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    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. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    3. Ma, Tai-Yu & Faye, Sébastien, 2022. "Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks," Energy, Elsevier, vol. 244(PB).
    4. repec:osf:socarx:q86sd_v1 is not listed on IDEAS
    5. Aurélien Froger & Ola Jabali & Jorge E. Mendoza & Gilbert Laporte, 2022. "The Electric Vehicle Routing Problem with Capacitated Charging Stations," Transportation Science, INFORMS, vol. 56(2), pages 460-482, March.
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