A review of spatial localization methodologies for the electric vehicle charging infrastructure

Abstract

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem's spatial dimension, and derives perspectives for further research. ARTICLE HISTORY

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

“…Pagany et al [22] gave a broad and systematic overview of publications in the domain of CS localization for EVs. The review categorized 661 studies based on their structure (user, destination, or route orientation), overarching objectives (like demand density, trip length, or queuing), and data sources (statistics, measured travel data, simulations or surveys) and discussed the advantages and limitations of the applied data and methods.…”

“…Pagany et al [22] concluded that a more integrated process should be the aim of further research on CS localization. Based on this, our study pursues a more holistic CS localization model, including EV drivers' dwelling times and destinations as well as the points of interest (POIs) as the model's key aspects.…”

Electric Vehicles in Jordan: Challenges and Limitations

“…In the transportation system, EV infrastructure resources, such as charging stations, parking lots, and roads, represent the supply side that electric vehicle owners (as the demand side) will take advantage of [7]. There are many dynamic factors (location, time, and scale) that could affect EV infrastructure resources and restrict the travel demand by EV drivers [8].…”

Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

“…At present, the application of LBS big data in the study of EV mobility or travel behavior is under exploration and mostly focuses on planning public charging stations. For example, Tu et al used the GPS data of EV ride-hailing groups to estimate charging demand [15]; Weldon et al used the GPS-tracked EV fleet in Ireland to support charging station planning and Pedge et al discussed the available data sources for charging behavior modelling [40,41]. Distinguishing between EV users and non-EV users and establishing an acceptable data security mechanism also become challenging when using these types of data.…”