Recognition of electric vehicles charging patterns with machine learning techniques

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Shekari, M., Arasteh, H. and Vahidinasab, V. ORCID: 0000-0002-0779-8727, 2022. Recognition of electric vehicles charging patterns with machine learning techniques. In: V. Vahidinasab ORCID: 0000-0002-0779-8727 and B. Mohammadi-Ivatloo, eds., Electric vehicle integration via smart charging: technology, standards, implementation, and applications. Green Energy and Technology . Cham: Springer, pp. 49-83. ISBN 9783031059087

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Official URL: https://doi.org/10.1007/978-3-031-05909-4_3

Abstract

In recent years, the utilization of electric vehicles (EVs) and renewable energy sources (RESs) are highly interested in supplying some parts of the required energy and paving the way for reaching other goals, such as emission reduction. However, uncontrolled energy management of the EVs’ high penetration may adversely affect the distribution system. The chapter aims to investigate the charging behavior of EVs. By analyzing the charging patterns of the EV stations, different rules could be developed to manage the EV charging patterns. This chapter introduced the machine learning (ML)-based approach to cluster the EV charging behaviors and improve the management of EVs by distinguishing the most representative charging patterns. Identifying clusters of EV charging patterns was conducted via the unsupervised learning ML method, while the supervised learning ML method was utilized for further classification of the dataset. An example was also used to demonstrate the effectiveness of the proposed method.

Item Type:

Chapter in book

Creators:

Shekari, M., Arasteh, H. and Vahidinasab, V.

Publisher:

Springer

Place of Publication:

Cham

Date:

2022

ISBN:

9783031059087