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Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling

Author

Listed:
  • Ahmad Almaghrebi

    (Durham School of Architectural Engineering & Construction, College of Engineering, University of Nebraska—Lincoln, Omaha, NE 68182, USA)

  • Kevin James

    (Durham School of Architectural Engineering & Construction, College of Engineering, University of Nebraska—Lincoln, Omaha, NE 68182, USA)

  • Fares Al Juheshi

    (Durham School of Architectural Engineering & Construction, College of Engineering, University of Nebraska—Lincoln, Omaha, NE 68182, USA)

  • Mahmoud Alahmad

    (Durham School of Architectural Engineering & Construction, College of Engineering, University of Nebraska—Lincoln, Omaha, NE 68182, USA)

Abstract

In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users’ behavior is imperative. This paper examines the trends in household charging sessions’ timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected from each session, a novel framework is introduced for the efficient, real-time prediction of important charging characteristics. Utilizing historical data and user-specific features, machine learning models are trained to predict the connection duration, charging duration, charging demand, and time until the next session. These models enhance the understanding of EV users’ behavior and provide practical tools for optimizing the EV charging infrastructure and effectively managing the charging demand. As the transportation sector becomes increasingly electrified, this work aims to empower stakeholders with insights and reliable models, enabling them to anticipate the localized demand and contribute to the sustainable integration of electric vehicles into the grid.

Suggested Citation

  • Ahmad Almaghrebi & Kevin James & Fares Al Juheshi & Mahmoud Alahmad, 2024. "Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling," Energies, MDPI, vol. 17(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:925-:d:1339867
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    References listed on IDEAS

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    1. Tim Jonas & Noah Daniels & Gretchen Macht, 2023. "Electric Vehicle User Behavior: An Analysis of Charging Station Utilization in Canada," Energies, MDPI, vol. 16(4), pages 1-19, February.
    2. Neaimeh, Myriam & Wardle, Robin & Jenkins, Andrew M. & Yi, Jialiang & Hill, Graeme & Lyons, Padraig F. & Hübner, Yvonne & Blythe, Phil T. & Taylor, Phil C., 2015. "A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts," Applied Energy, Elsevier, vol. 157(C), pages 688-698.
    3. Pramote Jaruwatanachai & Yod Sukamongkol & Taweesak Samanchuen, 2023. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach," Energies, MDPI, vol. 16(8), pages 1-22, April.
    4. Shin-Ki Hong & Sung Gu Lee & Myungchin Kim, 2020. "Assessment and Mitigation of Electric Vehicle Charging Demand Impact to Transformer Aging for an Apartment Complex," Energies, MDPI, vol. 13(10), pages 1-23, May.
    5. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
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    Cited by:

    1. Maher Alaraj & Mohammed Radi & Elaf Alsisi & Munir Majdalawieh & Mohamed Darwish, 2025. "Machine Learning-Based Electric Vehicle Charging Demand Forecasting: A Systematized Literature Review," Energies, MDPI, vol. 18(17), pages 1-92, September.
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    3. Emilia M. Szumska & Łukasz Pawlik & Damian Frej & Jacek Łukasz Wilk-Jakubowski, 2025. "Machine Learning Applications in Energy Consumption Forecasting and Management for Electric Vehicles: A Systematic Review," Energies, MDPI, vol. 18(20), pages 1-37, October.

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