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An optimal solution for charging management of electric vehicles fleets

Abstract : Electric vehicle (EV) is an advanced solution by car manufacturers to gradually replace the conventional vehicle and reduce our dependence on petroleum. Nevertheless, an EV need many hours for a full charge, and reducing charging time and energy consumption of EVs are among the major challenges for promoting this type of vehicles. Disturbed traffic conditions such as traffic jam, roads with sever slopes may affect seriously the energy consumption and then the performances of EVs. In this paper, scheduling and suitable assignment of EVs to charging stations (CSs) is approached as an optimization problem, formulated as linear programming problem. The assignment of EVs should satisfy certain constraints related to CSs status, the EV conditions, traffic conditions, etc. The proposed approach will be illustrated considering two operating modes of the system. The assignment of EVs to CSs under normal conditions (driving without using electrical accessories, roads without slops and traffic jam, etc.), and under disturbed conditions for the second mode. For this first scenario, the two main components of the system are supposed to be homogeneous (EVs have the same characteristics, and the same for CSs). For the second scenario, we focus on a charging system with heterogeneous components. As we will show, the suitable assignment of an EV is when the state of charge (SoC) of its battery remains at its highest possible level at the destination (assigned CS). Keeping the battery SoC at a high level allows to reduce consumed energy and required charging time, and consequently ensures a flexibility in the management of system charging. (C) 2016 Elsevier B.V. All rights reserved.
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https://hal-u-picardie.archives-ouvertes.fr/hal-03632408
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Soumis le : mercredi 6 avril 2022 - 12:03:54
Dernière modification le : jeudi 7 avril 2022 - 03:00:16

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R. Mkahl, Ahmed Nait-Sidi-Moh, J. Gaber, M. Wack. An optimal solution for charging management of electric vehicles fleets. Electric Power Systems Research, 2017, 146, pp.177-188. ⟨10.1016/j.epsr.2016.11.008⟩. ⟨hal-03632408⟩

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