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Recognition of Electric Vehicles Charging Patterns with Machine Learning Techniques

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Electric Vehicle Integration via Smart Charging

Part of the book series: Green Energy and Technology ((GREEN))

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.

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Notes

  1. 1.

    Nuvve is a publicly traded company accelerating transportation electrification through its proprietary V2G technology and offering charging and grid services.

  2. 2.

    Enel (Ente Nazionale per L’energia Elettrica) is an Italian multinational manufacturer and distributor of electricity and gas.

  3. 3.

    Insero is a company that wants to create sustainable growth and development within the energy, information technology, and EVs in Denmark’s local area.

  4. 4.

    The Nissan Motor Company is a Japanese multinational automobile manufacturer headquartered in Nishi-ku, Yokohama, Japan.

  5. 5.

    Mitsubishi Motors Corporation is a Japanese multinational automotive manufacturer headquartered in Minato, Tokyo, Japan.

  6. 6.

    High-dimensional spaces are used to model datasets with a large number of properties. A dataset can be represented directly in a space spanned by its features. Each record is represented as a point in space, and its location determined by the values of its attributes [95].

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Author information

Authors and Affiliations

  1. Power Systems Group, Department of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Mohammadreza Shekari

  2. Power Systems Operation and Planning Research Department, Niroo Research Institute (NRI), Tehran, Iran

    Hamidreza Arasteh

  3. Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, UK

    Vahid Vahidinasab

Authors
  1. Mohammadreza Shekari
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  2. Hamidreza Arasteh
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  3. Vahid Vahidinasab
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Correspondence to Hamidreza Arasteh .

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  1. Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, UK

    Vahid Vahidinasab

  2. Electrical and Electronics Engineering Department, Muğla Sıtkı Koçman University, Muğla, Turkey

    Behnam Mohammadi-Ivatloo

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Shekari, M., Arasteh, H., Vahidinasab, V. (2022). Recognition of Electric Vehicles Charging Patterns with Machine Learning Techniques. In: Vahidinasab, V., Mohammadi-Ivatloo, B. (eds) Electric Vehicle Integration via Smart Charging. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-05909-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-05909-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05908-7

  • Online ISBN: 978-3-031-05909-4

  • eBook Packages: EnergyEnergy (R0)

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