This study presents an approach to collect and classify usage data of public charging infrastructure in order to predict usage based on socio-demographic data within a city. The approach comprises data acquisition and a two-step machine learning approach, classifying and predicting usage behavior. Data is acquired by gathering information on charging points from publicly available sources. The first machine learning step identifies four relevant usage patterns from the gathered data using an agglomerative clustering approach. The second step utilizes a Random Forest Classification to predict usage patterns from socio-demographic factors in a spatial context. This approach allows to predict usage behavior at locations for potential new charging points. Applying the presented approach to Munich, a large city in Germany, results confirm the adaptability in complex urban environments. Visualizing the spatial distribution of the predicted usage patterns shows the prevalence of different patterns throughout the city. The presented approach helps municipalities and charging infrastructure operators to identify areas with certain usage patterns and, hence different technical requirements, to optimize the charging infrastructure in order to help meeting the increasing demand of electric mobility.

This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.

In this study, the data-driven simulation framework CDRpy is presented, validated, and applied using the example of the city of Munich to support decision makers in the rollout of public charging infrastructure in urban areas. The innovation of the study lies in how CDRpy can be used to evaluate different concepts for public charging infrastructure at a detailed level. Demand was mapped based on real charging events for different types of electric vehicles and pricing models, and the evaluations were broken down to the level of individual charging stations. The developed framework was validated using charge detail records from Munich’s largest public charging infrastructure operator for the years 2020, 2021, and 2022. The validation compared CDRpy simulation results with real usage data for time utilization, energy transferred per month, and energy transferred per subdistrict. In a further case study, the additional demand for public charging infrastructure in the city was estimated for the year 2030. The results indicate that mixed and time-based pricing models could reduce the required number of public charging points by around 10% compared with energy-based pricing models. Determining the expansion requirements for each subdistrict showed that the additional demand for public charging infrastructure varied greatly across the city. Since both technological and economic perspectives were considered, the study is relevant for all decision makers involved in the planning, operation, and scaling of public charging infrastructure.