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

Ahmad Almaghrebi (), Kevin James, Fares Al Juheshi and Mahmoud Alahmad ()
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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

Energies, 2024, vol. 17, issue 4, 1-20

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.

Keywords: plug-in electric vehicle; household charging stations; charging behavior; machine learning; data driven (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: 2024
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