Durham School of Architectural Engineering and Construction


First Advisor

Mahmoud Alahmad

Date of this Version

Summer 8-2023

Document Type



Almaghrebi, A. (2023). Data-Driven Framework for Predicting and Scheduling Household Charging of EVs (Doctoral dissertation, The University of Nebraska-Lincoln).


A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Architectural Engineering, Under the Supervision of Professor Mahmoud Alahmad. Lincoln, Nebraska: August 2023

Copyright © 2023 Ahmad Almaghrebi


The increasing prevalence of EV charging poses challenges for power grid stability and quality due to high charging load demands. Without effective energy management strategies for EV charging, the simultaneous power demand from numerous EVs can strain the electric grid, impacting power quality and the wholesale electricity market. To address these challenges, this dissertation presents a comprehensive framework comprising five critical tasks: analyzing EV charging behavior, optimizing charging schedules, developing predictive models, analyzing aggregated impacts, and evaluating implications of predicted user behavior on scheduling. By examining EV charging behavior at household and public charging stations, this study aims to understand patterns and variations in charging sessions. The framework introduces a centralized scheduling approach for household charging stations to reduce peak demand and costs, relying on accurate knowledge of EV charging behavior. Machine learning and linear regression models are utilized to predict session charging parameters, with Random Forest models outperforming other methods, yet uncertainties persist in the predictions. The study also investigates aggregate demand and connectivity of multiple EV users, revealing the potential to predict aggregate trends by incorporating session predictions. Evaluating the day-ahead scheduling framework implemented with predicted charging data using actual data highlights challenges in meeting user demand due to prediction errors. This research provides valuable insights into EV charging behavior, emphasizing the significance of accurate data for strategic scheduling. While machine learning models show potential in predicting EV charging behavior, limited correlation between session variables and available information at plug-in is observed. Challenges arise from errors in session predictions when scheduling EV charging for a group of users, suggesting the need for a more decentralized approach.

Advisor: Mahmoud Alahmad