A Review of Electric Vehicle Load Open Data and Models

Abstract

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.

A review of electric vehicle charging session open data

“…Various distributions have been considered in the literature to model these variables. Namely, Gaussian, exponential, lognormal, Weibull and gamma distributions [2]. The variety of datasets found provides an ideal setup to check whether the distributional assumptions made in the literature can be applied to various datasets.…”

“…Therefore, anticipating the wide adoption of EVs requires an understanding of EV charging behaviours. A variety of EV load models have been proposed in the literature [2]. One obstacle to the production of such models is the lack of reliable data.…”

“…In this work, an open data search focused on finding EV charging session information is presented. This endeavour was undertaken in order to foster reproducible work in the field and is an extension of [2] which reviews EV load models. The rest of the poster is structured as follows: Section 2 details the method and results of the open data search.…”

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

“…For example, at the end of 2020 there were only 51 public charging points in Manhattan, New York City (51 Level 2 and 0 Level 3 chargers). 1 The limited number of fast/rapid public chargers has become one of the major obstacles for widespread EV adoption (Engel et al, 2018). As there are more and more EVs running on the road, it becomes a struggle to find a charging point before running out of battery.…”

Harmonic and Supraharmonic Emissions of Plug-In Electric Vehicle Chargers

“…It is recognized that simulated charging patterns and profiles on the one hand may be realistic, representative and quite close to the measured data available, and on the other hand, may allow the application of induced artificial variability, probabilistic behavior and statistical dispersion, which would not otherwise be possible with purely experimental data. The accuracy and reliability of simulated data and synthetic time series are discussed among others in [14,15], focusing on EV load, charge profile and locations, without exploring the details of the EV electric behavior. This was carried out in [16], integrating EV and LV distribution grid models from a probabilistic perspective.…”