LAPSE:2023.25350v1
Published Article

LAPSE:2023.25350v1
Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants
March 28, 2023
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.
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.
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Keywords
AIMD, distributed control, distributed management, electric vehicles, optimal scheduling
Subject
Suggested Citation
Ravasio M, Incremona GP, Colaneri P, Dolcini A, Moia P. Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants. (2023). LAPSE:2023.25350v1
Author Affiliations
Ravasio M: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Incremona GP: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy [ORCID]
Colaneri P: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; IEIIT-CNR, 20133 Milan, Italy [ORCID]
Dolcini A: Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy
Moia P: Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy
Incremona GP: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy [ORCID]
Colaneri P: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; IEIIT-CNR, 20133 Milan, Italy [ORCID]
Dolcini A: Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy
Moia P: Alstom SESTO, Via Fosse Ardeatine, 120, 20099 Sesto San Giovanni, Italy
Journal Name
Energies
Volume
14
Issue
15
First Page
4389
Year
2021
Publication Date
2021-07-21
ISSN
1996-1073
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Original Submission
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PII: en14154389, Publication Type: Journal Article
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LAPSE:2023.25350v1
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https://doi.org/10.3390/en14154389
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Mar 28, 2023
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