# A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations
## 1 Introduction  
European governments are focused on greatly reducing the transport sector's carbon emissions. An element that plays a key role is the electric vehicle (EV) and the expected uptake of the EV market [1]. The electric vehicle, powered in whole or in part by electricity from electricity grids, is more efficient and environmentally friendly (i.e., results in lower emissions of gases and noise) when compared to other current propulsion technologies and current vehicles. Among the modes of transportation in cities that need solutions for their migration from emitting technologies to non-polluting alternatives such as electricity, we must take into account public vehicles (i.e., buses, taxis, postal service vehicles, rental vehicles) as well as as private vehicles (i.e., cars, motorcycles, bicycles, mototaxis).  
Currently, consumers can choose from an wide number of electric vehicle models that provide many environmental benefits. According to the European Environment Agency, although the number of electric vehicle sales has increased rapidly over past years, they represented just 1.2% of all new cars sold in the EU in 2015 [2].  
The causes that can hinder the introduction of EV use in cities include the following: limited information and technological uncertainty (in comparison with the more familiar conventional vehicle technologies), the limitations of battery life and charging times of EVs, and the lack a charging infrastructure that covers the potential demand for the EVs [3]. The last cause is closely related to range anxiety that is considered a significant obstacle to market acceptance of EVs [4]. Range anxiety is the fear that the vehicle has insufficient range to reach the destination [5]. An option to deal with this problem is through the deployment of an efficient public charging infrastructure. However, since infrastructure development is expensive, there is a need to direct investments towards the establishment of refueling facilities in areas with maximum impact (i.e., maximum coverage at a low cost).  
In this paper, the aim is to provide a multi-agent system (MAS) for the planning of efficiently located infrastructures for electric vehicle charging stations for the public and private sectors in a city. For this purpose, we present an MAS that integrates the collection of information from heterogeneous data sources and the optimization of the charging station locations using artificial intelligence algorithms. In this way, the proposed MAS allows the evaluation of a set of possible configurations of charging station locations. The MAS also considers different configurable criteria and determines which is the most advisable configuration of charging station locations.  
This proposal will contribute in the research and technology transfer of new analysis systems for the strategic planning and distribution of urban elements, such as charging stations for EVs. The proposed system will make it possible to: (1) guarantee the supply of electricity for charging EVs in the city; and (2) optimize investment in infrastructures and charging stations, which results in greater sustainability.  
The problem lies in where should the charging stations should be established and how many charging piles should be established in each charging station in order to provide service to as many EV users as possible.  
The article is structured in the following sections. Section 2 presents previous works related to the proposal presented. Section 3 describes the main aspects of the proposed MAS and details the _Emplacement Optimizer Agent_, which is the core of the system. Section 4 presents a case study for the city of Valencia, and Section 5 shows some experimental results over the proposed case study. Finally, Section 6 provides some conclusions and information on future works.