Methodology for the optimized localization of charging infrastructure for electric vehicles in urban environments

Abstract

Transitioning to sustainable mobility is a key strategy in addressing climate change and reducing urban emissions. Battery electric vehicles are rapidly gaining market share, driven by technological advancements, policy incentives, and increasing environmental awareness among consumers. The success of this technology is heavily dependent on the availability of a wellplanned charging infrastructure. There is a research gap in generally applicable methods for planning charging infrastructure. As a consequence, the market penetration of battery electric vehicles could be significantly hindered by inadequate charging networks. To address this challenge, a novel methodology is proposed for the planning of urban charging infrastructure using open-source data. The proposed approach combines a demand-based analysis with geospatial criteria to optimize the placement of future charging points in urban areas. Based on real-world data, the overall number of required charging points is calculated. For each charging point the service area is calculated, which quantifies the demand coverage. Further important criteria are supply of candidate locations, the distribution of demand and the existing charging points. The developed methodology weights all criteria with the TOPSIS multi-criteria decision making algorithm and creates a demand map. This is then used to find the optimal location for new charging points, using a proprietary placement algorithm. As a result, in a case study of Berlin (Germany), more than 82% of the local charging demand could be met. The novel approach provides stakeholders with a solid foundation for charging infrastructure plans, supporting the rapid adoption of battery electric vehicles and meeting future mobility demands.