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Electric Vehicle Smart Charging Reservation Algorithm.

Radu FloceaAndrei HîncuAndrei RobuStelian SenocicoAndrei TraciuBaltariu Marian RemusMaria Simona RaboacaConstantin Filote
Published in: Sensors (Basel, Switzerland) (2022)
The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impeding this process, ranging from insufficient charging infrastructure, battery capacity, and long queueing and charging times, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fuelled by the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that bypasses the limitations of the "reserve now" function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station's reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations and the occupation of several stations without queues, assuring, thus, a proper distribution of the available energy resources, while increasing end-user satisfaction. Our solution is independent from the OCPP reservation method; therefore, the authentication and reservation processes performed by the proposed algorithm run only through the central system, authorizing only the creator of the reservation to start the charging transaction.
Keyphrases
  • machine learning
  • air pollution
  • deep learning
  • neural network
  • cystic fibrosis
  • solid state