The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, to review electric vehicle charging load models with their strengths and weaknesses. Third, to provide suggestions on matching the models reviewed to six datasets found in this research that have not previously been explored in the literature. The open data search covered more than 860 repositories and yielded around 60 datasets that are relevant for modelling electric vehicle charging load. These datasets include information on charging point locations, historical and real-time charging sessions, traffic counts, travel surveys and registered vehicles. The models reviewed range from statistical characterization to stochastic processes and machine learning and the context of their application is assessed.

The need for reliable and accessible electric vehicle (EV) charging data is becoming increasingly important as governments and industries aim to create low-carbon transport systems. Without careful grid management, the security of supply could be compromised. In this work, the results of an open data search are presented with 8 open charging session datasets highlighted and discussed from a practical perspective. These datasets cover a range of charging options (residential, workplace and public) and it is shown that they are all relevant for developing EV load models. To aid with this future modelling work, a distributional analysis of the main influential factors of EV charging load is provided. CCS CONCEPTS• Hardware → Smart grid; • Mathematics of computing → Maximum likelihood estimation; • General and reference → Empirical studies.