@article{FischerMichalkHardtetal.2024, author = {Fischer, Markus and Michalk, Wibke and Hardt, Cornelius and Bogenberger, Klaus}, title = {Bill It Right: Evaluating Public Charging Station Usage Behavior under the Presence of Different Pricing Policies}, series = {World Electric Vehicle Journal}, volume = {15}, journal = {World Electric Vehicle Journal}, number = {4}, doi = {10.3390/wevj15040175}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{FischerHardtMichalketal.2022, author = {Fischer, Markus and Hardt, Cornelius and Michalk, Wibke and Bogenberger, Klaus}, title = {Charging or Idling: Method for quantifying the Charging and the Idle Time of public Charging Stations}, series = {TRB 101st Annual Meeting Compendium of Papers, Washington DC, USA}, booktitle = {TRB 101st Annual Meeting Compendium of Papers, Washington DC, USA}, pages = {22}, year = {2022}, abstract = {The study at hand presents a methodology to determine the utilization of public charging infrastructure by the proportion of effective charging time and idle time. The results of the study can be used by decision makers to better understand the usage behavior of public charging infrastructure and to align the provision of charging infrastructure with the behavior of users. The user behavior is analyzed using charging areas and scenario-based analysis of charging sessions. More than 300,000 charging sessions at AC and DC charging stations in the urban area of Munich in 2020 were recorded as the data basis for the study. The methodology takes the available vehicle models in the study area as well as the characteristic charging behavior of plug-in electric vehicles (PEV) with a decreasing charging power at high states of charge of the battery into account. The results show a high proportion of idle time at AC charging stations. The outcome of a correlation analysis additionally indicates a correlation between the share of idle time and the proportion of high-density urban living in the survey area.}, language = {en} } @article{FrieseMichalkFischeretal.2021, author = {Friese, Philipp A. and Michalk, Wibke and Fischer, Markus and Hardt, Cornelius and Bogenberger, Klaus}, title = {Charging Point Usage in Germany—Automated Retrieval, Analysis, and Usage Types Explained}, series = {Sustainability}, volume = {13}, journal = {Sustainability}, number = {23}, publisher = {MDPI}, doi = {10.3390/su132313046}, pages = {26}, year = {2021}, abstract = {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.}, language = {en} }