A Review of Electric Vehicle Load Open Data and Models

Yvenn Amara-Ouali, Yannig Goude, Pascal Massart, Jean-Michel Poggi and Hui Yan
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Yvenn Amara-Ouali: Laboratoire de Mathématiques d’Orsay (LMO), CNRS, Faculté des Sciences d’Orsay, Université Paris-Saclay, 91405 Orsay, France
Yannig Goude: Laboratoire de Mathématiques d’Orsay (LMO), CNRS, Faculté des Sciences d’Orsay, Université Paris-Saclay, 91405 Orsay, France
Pascal Massart: Laboratoire de Mathématiques d’Orsay (LMO), CNRS, Faculté des Sciences d’Orsay, Université Paris-Saclay, 91405 Orsay, France
Jean-Michel Poggi: Laboratoire de Mathématiques d’Orsay (LMO), CNRS, Faculté des Sciences d’Orsay, Université Paris-Saclay, 91405 Orsay, France
Hui Yan: EDF Lab, 7 bd Gaspard Monge, 91120 Palaiseau, France

Energies, 2021, vol. 14, issue 8, 1-35

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.

Keywords: electric vehicles; charging point; load modelling; smart charging; open data; statistical modelling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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