A Model for the Data-based Analysis and Design of Urban Public Charging Infrastructure

With electromobility on the rise, several re-searchers have acknowledged the charging station location problem as a major challenge for city planners and decision-makers. Especially in urban areas, where there is little room for privately owned charging infrastructure, public charging infrastructure is relevant to users of battery electric vehicles. This paper reviews current literature on the charging station location problem and proposes a four-step model concept for the placement and allocation of urban public charging sites based on the shortcomings of existing approaches. The proposed model combines the advantages of existing approaches and is exclusively built on socio-demographic and geospatial data that is readily available in all larger municipalities of the world. The model consists of a demand estimation based on socio-demographic and geospatial data, a candidate site selection based on a genetic algorithm, an agent-based infrastructure simulation to account for user behavior and system dynamics, and a heuristic repositioning approach to optimize charger allocation.