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Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants

Author

Listed:
  • Matteo Ravasio

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Gian Paolo Incremona

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Patrizio Colaneri

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
    IEIIT-CNR, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Andrea Dolcini

    (Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy
    These authors contributed equally to this work.)

  • Piero Moia

    (Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy
    These authors contributed equally to this work.)

Abstract

Recently, the introduction of electric vehicles has given rise to a new paradigm in the transportation field, spurring the public transport service in the direction of using completely electric bus fleets. In this context, one of the main challenges is that of guaranteeing an optimal scheduling of the charging process, while reducing the power supply requested from the main grid, and improving the efficiency of the resource allocation. Therefore, in this paper, a power allocation strategy is proposed in order to optimize the charging of electric bus fleets, while fulfilling the limitation imposed on the maximum available power, as well as ensuring limited charging times. Specifically, relying on real bus charging scenarios, a charging optimization algorithm based on a Nonlinear Additive Increase Multiplicative Decrease (NAIMD) strategy is proposed and discussed. This approach is designed on the basis of real charging power curves related to the batteries of the considered vehicles. Moreover, the adopted NAIMD algorithm allows us to minimize the sum of charging times in the presence of saturation constraints in a distributed way and with a small amount of aggregated data sent over the communication network. Finally, an extensive simulation campaign is illustrated, showing the effectiveness of the proposed approach both in allocating the power resources and in sizing the maximum power capacity of charging plants in progress.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4389-:d:598148
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    References listed on IDEAS

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