Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants

Matteo Ravasio, Gian Paolo Incremona, Patrizio Colaneri, Andrea Dolcini and Piero Moia
Additional contact information
Matteo Ravasio: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Gian Paolo Incremona: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Patrizio Colaneri: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Andrea Dolcini: Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy
Piero Moia: Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy

Energies, 2021, vol. 14, issue 15, 1-17

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.

Keywords: AIMD; distributed control; electric vehicles; optimal scheduling; distributed management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/15/4389/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/15/4389/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:15:p:4389-:d:598148

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().