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Optimization and Coordination of Electric Vehicle Charging Process for Long-Distance Trips

Author

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  • Jean Hassler

    (Laboratoire de Génie Electrique et Electronique de Paris, Sorbonne Université, CNRS, 75252 Paris, France
    Groupe PSA, Route de Gisy, 78140 Vélizy-Villacoublay, France
    Laboratoire de Génie Electrique et Electronique de Paris, Université Paris-Saclay, CentraleSupélec, CNRS, 91192 Gif-sur-Yvette, France)

  • Zlatina Dimitrova

    (Groupe PSA, Route de Gisy, 78140 Vélizy-Villacoublay, France)

  • Marc Petit

    (Laboratoire de Génie Electrique et Electronique de Paris, Sorbonne Université, CNRS, 75252 Paris, France
    Laboratoire de Génie Electrique et Electronique de Paris, Université Paris-Saclay, CentraleSupélec, CNRS, 91192 Gif-sur-Yvette, France)

  • Philippe Dessante

    (Laboratoire de Génie Electrique et Electronique de Paris, Sorbonne Université, CNRS, 75252 Paris, France
    Laboratoire de Génie Electrique et Electronique de Paris, Université Paris-Saclay, CentraleSupélec, CNRS, 91192 Gif-sur-Yvette, France)

Abstract

Battery electric vehicles offer many advantages in terms of performance and zero-emission pollutants, but their limited range for long-distance trips compromises their large-scale market penetration. The problem of range can be solved with a dense network of fast-charging stations and an increase in embedded battery capacity. Simultaneously, improvements in high-power charging point units offer range gains of hundreds of kilometers in a mere 20 min. One risk remains: The travel time depends on the availability of charging stations, which can drop during rush hours, due to long queues, or power grid constraints. These situations could significantly affect the user experience. In this paper, we presented an approach to coordinate EV charging station choices in the case of long-distance trips. This system relies on vehicle-to-infrastructure communications (V2X). The objective is to enhance the use of the infrastructure by improving the distribution of vehicles between the different charging stations, thus reducing waiting time. Our target is to build an efficient and easily deployable system. The performance of this system is compared to an uncoordinated situation and an offline optimization. We conducted a case study on a 550-km highway with heavy traffic. With this system, the results showed a 10% reduction in time spent in charging stations.

Suggested Citation

  • Jean Hassler & Zlatina Dimitrova & Marc Petit & Philippe Dessante, 2021. "Optimization and Coordination of Electric Vehicle Charging Process for Long-Distance Trips," Energies, MDPI, vol. 14(13), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4054-:d:588899
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    References listed on IDEAS

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    Cited by:

    1. Matteo Ravasio & Gian Paolo Incremona & Patrizio Colaneri & Andrea Dolcini & Piero Moia, 2021. "Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants," Energies, MDPI, vol. 14(15), pages 1-17, July.

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