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Smart Charging for Electric Car-Sharing Fleets Based on Charging Duration Forecasting and Planning

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Listed:
  • Francesco Lo Franco

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

  • Vincenzo Cirimele

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

  • Mattia Ricco

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

  • Vitor Monteiro

    (Department of Industrial Electronics, University of Minho, Azurem, 4800-058 Guimarães, Portugal)

  • Joao L. Afonso

    (Department of Industrial Electronics, University of Minho, Azurem, 4800-058 Guimarães, Portugal)

  • Gabriele Grandi

    (Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy)

Abstract

Electric car-sharing (ECS) is an increasingly popular service in many European cities. The management of an ECS fleet is more complex than its thermal engine counterpart due to the longer ”refueling“ time and the limited autonomy of the vehicles. To ensure adequate autonomy, the ECS provider needs high-capacity charging hubs located in urban areas where available peak power is often limited by the system power rating. Lastly, electric vehicle (EV) charging is typically entrusted to operators who retrieve discharged EVs in the city and connect them to the charging hub. The timing of the whole charging process may strongly differ among the vehicles due to their different states of charge on arrival at the hub. This makes it difficult to plan the charging events and leads to non-optimal exploitation of charging points. This paper provides a smart charging (SC) method that aims to support the ECS operators’ activity by optimizing the charging points’ utilization. The proposed SC promotes charging duration management by differently allocating powers among vehicles as a function of their state of charge and the desired end-of-charge time. The proposed method has been evaluated by considering a real case study. The results showed the ability to decrease charging points downtime by 71.5% on average with better exploitation of the available contracted power and an increase of 18.8% in the average number of EVs processed per day.

Suggested Citation

  • Francesco Lo Franco & Vincenzo Cirimele & Mattia Ricco & Vitor Monteiro & Joao L. Afonso & Gabriele Grandi, 2022. "Smart Charging for Electric Car-Sharing Fleets Based on Charging Duration Forecasting and Planning," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12077-:d:923962
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    References listed on IDEAS

    as
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    4. Francesco Lo Franco & Mattia Ricco & Riccardo Mandrioli & Gabriele Grandi, 2020. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization," Energies, MDPI, vol. 13(19), pages 1-25, September.
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