IFAC PapersOnLine 51-9 (2018) 206–211 ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2018.07.034 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. 1. INTRODUCTION In recent years, traffic developments have shown that we have to rethink the way we handle the mobility needs of people and goods to work towards a better environmental compatibility. In particular, particulate matter pollution in city centers and the global warming based on greenhouse gas emissions are two major challenges for modern society that are closely linked to (road) traffic based on internal combustion engines. Electric mobility, in terms of battery electric vehicles (BEV), can be one partial solution to address these challenges and is therefore currently being promoted by many different stakeholders. The focus lies on expanding the technology nationwide and in a timely manner. However, in January 2017 only 34.022 BEVs were registered in Germany [KBA 2017], which hints at major impediments to the adoption of the novel technology that needs to be coped with. The public reluctance of adopting elec- tric vehicles can be explained by several reasons. On the one hand, socio-economic factors might play a significant role with respect to the higher purchase and maintenance costs [Hanke et al., 2014; Sierzschula et al., 2014]. On the other hand, the public perception of electric vehicles as a novel and not mature technology, in contrast to the familiar combustion engine ve- hicles, might also impede a seamless adoption [Peters & Dütschke, 2014; Ziefle et al., 2014; Zaunbrecher et al., 2014], at least in Germany, a country with a long and deeply anchored combustion vehicle tradition [Kirsch 2000]. In this context, range preferences and range anxiety in electric vehicles seems 1 STELLA is the acronym for the German term "STandortfindungsmodell für ELektrische LAdeinfrastruktur”, meaning a model for localiza- tion of electric charging infrastructure. to play a key role in the reluctance to adopt battery electric vehicles [Franke & Krems, 2013; Rauh et al., 2015]. Conse- quently and in addition to the ongoing technical improvement of the vehicles, the expansion of the charging infrastructure (CIS) contributes significantly to the concern of potential users that the range of the batteries might not be enough for the dis- tances they want to cover [Kim et al. 2017] and drivers´ con- cerns that there might be no CIS available for recharging when necessary [Halbey et al. 2015]. Therefore, the identification of optimal locations for fast- charging infrastructure is crucial to establish both a ubiquitous and a need-based charging network, which covers the individ- ual mobility needs of all car drivers and which is commercially viable in the long term. 2. RELATED WORK First, it can be stated that planned and promptly deployed fast- charging networks can compete with networks that arise over a longer period of time by the market entry and exit of diverse commercial providers and which are regulated by the rules of a free market economy [Bernardo et al. 2013]. Bernardo et al. also stated, that organically grown networks are often characterised by a dense clustering in local areas, while planned networks have a more uniform coverage and can therefore cope with area-wide demands. Further, they used the Keywords: site identification, electric charging infrastructure, requirement analysis, electric mobility. Abstract: The spread of a charging infrastructure for battery electric vehicles is crucial for both coping with the increasing number of electric vehicles and reducing users’ impediments to adoption. Therefore, the selection of optimal (fast-)charging locations determines acceptance, utilization, and thus, the economic viability of a single site or the whole charging network. The methodology of the Integrated Model Ap- proach STELLA 1 for site identification for a charging infrastructure transmits proven methods of traffic modeling in a new context. It is already possible to make statements regarding the positioning of the charg- ing infrastructure as well as the expectable quantitative needs of charging stations during the early stages of infrastructure planning processes to target a nationwide, ubiquitous and needs-based charging network, by combining different data (e. g. transport networks and traffic volumes, settlement structures, vehicle characteristics, power supply data and user requirements) and refining classic four-step traffic modelling. Due to the structure of the method, a flexible combination, extension or transferability to other contents is possible. Currently, this method has been developed for a planning area that covers the entire territory of the Federal Republic of Germany. *Chair and Institute of Urban and Transportation Planning (ISB), RWTH Aachen University, Germany (e-mail: brost@isb.rwth-aachen.de). **Chair of Communication Science, Human-Computer Interaction Center (HCIC), RWTH Aachen University, Germany Waldemar Brost*, Teresa Funke*, Ralf Philipsen**, Teresa Brell**, Martina Ziefle** Integrated Model Approach STELLA Method of Site Identification for Charging Infrastructure