An Open-Source Model for Estimating the Need to Expansion in Local Charging Infrastructures

The growing adoption of electric vehicles (EVs) poses new challenges for the planning and management of charging infrastructures (CIs). This paper proposes a methodology to estimate the sufficiency of EV charging infrastructures in a given area of study (AOS) containing public and private buildings, using open-source data and a case study of Lindau (Bodensee), Germany. The methodology consists of two main steps: first, applying the attractiveness factor concept from travel models to cluster buildings according to their potential EV users; second, classifying charging stations based on their location and occupancy rate. To reach our desired result, we compare the number of charging hours needed by EVs arriving at each building cluster with the number of available charging stations in each station cluster, and identify any gaps or surpluses. The paper demonstrates the feasibility and applicability of the methodology using data from the city Lindau (Bodensee) as an example. The paper also discusses the limitations and assumptions of the methodology, and suggests future directions for developing a machine-learning based tool that could support optimal placement of new charging stations.