Sistema Inteligente De Recarga De Vehículos Eléctricos: Diseño Y Operación

Franco, Javier Sedano; Garcia, Miguel Portal; Arauzo, Alejandro Hernandez; Flecha, José Ramón Villar; Peinador, Jorge Puente; Arias, Ramiro Varela

doi:10.6036/5788

Cited by 5 publications

(5 citation statements)

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“…As mentioned, the model considered in this work is that proposed in [8]. This model corresponds to the charging station described in [39], which is designed to be installed in community parking. In this station, a parking lot has a charging point connected to one of the lines of a three-phase feeder.…”

Section: Literature Reviewmentioning

confidence: 99%

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Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

et al. 2018

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Scheduling the charging times of a large fleet of Electric Vehicles (EVs) may be a hard problem due to the physical structure and conditions of the charging station. In this paper, we tackle an EV’s charging scheduling problem derived from a charging station designed to be installed in community parking where each EV has its own parking lot. The main goals are to satisfy the user demands and at the same time to make the best use of the available power. To solve the problem, we propose an artificial bee colony (ABC) algorithm enhanced with local search and some mating strategies borrowed from genetic algorithms. The proposal is analyzed experimentally by simulation and compared with other methods previously proposed for the same problem. The results of the experimental study provided interesting insights about the problem and showed that the proposed algorithm is quite competitive with previous methods.

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Section: Literature Reviewmentioning

confidence: 99%

“…The design and operation modes of the charging station is detailed in [39]; Figure 1 shows its general structure. The station is fed by three-phase electric power with voltage between phases of 400 V. Each charging point is connected to one single-phase at 230 V and 7.3 kW (32 A).…”

Section: The Charging Stationmentioning

confidence: 99%

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

et al. 2018

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“…As mentioned earlier, our problem is motivated by a real charging station, described in detail by Sedano et al (2013). The station was designed to be installed in a community car park consisting of privately-owned parking spaces .…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Here we consider a problem that consists in scheduling the charging of a set of EVs taking into account the characteristics of a real-life charging station described in Sedano et al (2013). The station has been designed to be installed in private car parks under simplicity and economy criteria.…”

Section: Introductionmentioning

confidence: 99%

Genetic fuzzy schedules for charging electric vehicles

García-Álvarez

González-Rodríguez

Vela

et al. 2018

Computers & Industrial Engineering

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This work tackles the problem of scheduling the charging of electric vehicles in a real-world charging station subject to a set of physical constraints, with the goal of minimising the total tardiness with respect to a desired departure date given for each vehicle. We model a variant of the problem that incorporates uncertainty in the charging times using fuzzy numbers. As solving method, we propose a genetic algorithm with tailor-made operators, in particular, a new chromosome evaluation method based on generating schedules from a priority vector. Finally, an experimental study avails the proposed genetic algorithm both in terms of algorithm convergence and quality of the obtained solutions.

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“…Initially, the agent considers public parking and garages to be PoIs. These PoIs are visited by a high number of users, usually the duration of stay is enough to charge a EV, and the power contracted in the installation is suitable for EV charging stations [25]. The consideration of these initial locations will not only increase the utilization but also increase the visibility, which might help to relieve range anxiety and promote EV acceptance.…”

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confidence: 99%

A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations

et al. 2018

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Abstract:One of the main current challenges of electric vehicles (EVs) is the creation of a reliable, accessible and comfortable charging infrastructure for citizens in order to enhance demand. In this paper, a multi-agent system (MAS) is proposed to facilitate the analysis of different placement configurations for EV charging stations. The proposed MAS integrates information from heterogeneous data sources as a starting point to characterize the areas where charging stations could potentially be placed. Through a genetic algorithm, the MAS is able to analyze a large number of possible configurations, taking into account a set of criteria to be optimized. Finally, the MAS returns a configuration with the areas of the city that are considered most appropriate for the establishment of charging stations according to the specified criteria.

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Sistema Inteligente De Recarga De Vehículos Eléctricos: Diseño Y Operación

Cited by 5 publications

References 0 publications

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

Genetic fuzzy schedules for charging electric vehicles

A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations

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