A comprehensive review of clustering methods applications in electric mobility

Abstract

The continuous growth of electric vehicles (EVs) has been boosted by the need to achieve society’s decarbonization targets. The mass adoption of EVs introduces new challenges in the power systems planning and operation, mainly due to the uncertainty related to EV users’ behavior and charging needs. Some of the difficulties motivated by the uncoordinated behavior of EVs are the occurrence of voltage instabilities, system overcurrents, and harmonic distortion. In this context, clustering can help better understand and categorize the behavior of EVs and electric vehicle supply equipment (EVSE) usage, with multiple research studies devoted to the study of clustering methods to offer solutions for these problems. This manuscript comprehensively presents a review of clustering methods applications for electric mobility that focus on the possibility of identifying different groups of EV charging processes, through clustering, to provide support in characterizing EV charging profiles, EV user behavior, and EVSE accessibility and location. For that, we present a roadmap that starts with cluster analysis, in which the most utilized mathematical clustering and validation techniques are detailed. Then, several EV charging datasets are described, followed by a review of research works focusing on clustering applications in EV data, considering three main categories, namely EV charging profiles, EV user behavior, and EVSE accessibility and location.