Electric vehicle charging stations emplacement using genetic algorithms and agent-based simulation. (1st July 2022)

• Record Type: Journal Article
• Authors: Jordán, Jaume; Palanca, Javier; Martí, Pasqual; Julian, Vicente
• Is Part Of: Expert systems with applications. Volume 197(2022)
• Publication Date: 2022-07-01
• Subjects: Genetic algorithm -- Electric vehicle -- Charging station -- Mobility -- Agent-based simulation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116739
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The increasingly evident incorporation of the electric vehicle in urban environments is an already undeniable change. Electric vehicles are appearing on the market with more autonomy and lower prices, which is facilitating the progressive change of the vehicle fleet. However, the electric vehicle brings with it the need to provide enough charging stations distributed throughout the city, so that the autonomy of the vehicle is not a problem. This work presents how a genetic algorithm that analyzes the open data sources of a city is used to propose the most suitable locations for these stations. This proposal is the input for a series of experiments that simulate the impact that has the placement of these stations along the city, in order to measure the benefits of the solution proposed by the genetic algorithm. To do this, an agent-based simulation infrastructure was built around a fleet simulator. Highlights: Genetic algorithm for the emplacement of electric vehicle charging stations. Development of an appropriate infrastructure for an agent-based simulation. Simulation of mobility based on real data of a city. Experimental results show that our solution improves the waiting time metric.