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Automatic control and dispatching of charging currents to a charging station for power-assisted bikes

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  • Nkounga, Willy Magloire
  • Ndiaye, Mouhamadou Falilou
  • Cisse, Oumar
  • Grandvaux, Françoise
  • Tabourot, Laurent
  • Ndiaye, Mamadou Lamine

Abstract

This work deals with the automatic dispatching of the charging currents in a charging station for power-assisted bikes (ebike). The decision variables such as arduousness index and urgency are determined. The arduousness index is carried out from the GPS ride data. Urgency is calculated using the parking time and ebike batteries state of charge. They are used to determine ebike's charging priorities at the charging station using continue fuzzy logic. Photovoltaic power forecasting is determined over the control horizon using the artificial neural network. On the one hand, the values of the priority, the photovoltaic power forecasting and the storage battery's state of charge are calculated. They allow to control the states of the switches associated with each charging spot and the operating mode of the storage battery (source or load) using discrete fuzzy logic. On the other hand, the interest of the ride's arduousness for a charging station is presented. A comparative study between the charging method integrating the ride arduousness and not is carried out. A case study of the polytech Annecy campus at the University of Savoie Mont Blanc in France is proposed. Results show that: the arduousness index is essential for controlling the charging priority of ebikes at the charging station; Fuzzy logic allows to manage the current dispatching on a charging station; taking into account the ride's arduousness allows to save up to 413.03 (Wh) of profit and 97.90% energy flexibility on the charging station.

Suggested Citation

  • Nkounga, Willy Magloire & Ndiaye, Mouhamadou Falilou & Cisse, Oumar & Grandvaux, Françoise & Tabourot, Laurent & Ndiaye, Mamadou Lamine, 2022. "Automatic control and dispatching of charging currents to a charging station for power-assisted bikes," Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222003188
    DOI: 10.1016/j.energy.2022.123415
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    1. Ullah, Zia & Wang, Shaorong & Wu, Guan & Hasanien, Hany M. & Rehman, Anis Ur & Turky, Rania A. & Elkadeem, Mohamed R., 2023. "Optimal scheduling and techno-economic analysis of electric vehicles by implementing solar-based grid-tied charging station," Energy, Elsevier, vol. 267(C).

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