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Deep charge-fusion model: Advanced hybrid modelling for predicting electric vehicle charging patterns with socio-demographic considerations

Lookup NU author(s): Dr Muhammed Cavus, Dr Huseyin Ayan, Emerita Professor Margaret Carol Bell CBE, Dr Oluwole Oyebamiji, Dr Dilum Dissanayake

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).

Abstract

This study examines electric vehicle (EV) charging behaviors across 5 898 participants from various socio-demographic backgrounds, using both statistical analysis and deep learning models. Exploratory data analysis (EDA) and analysis of variance (ANOVA) show that factors such as gender, education, employment status, household size, income, and housing type significantly influence charging behaviors at home, work, and fast-charging stations. Education notably impacts home charging (F = 2.54, p = 0.04), while household income strongly affects fast charging behavior (F = 5.34, p = 0.001). A hybrid deep learning model, deep charge-fusion, was developed by combining convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and extreme gradient boosting (XGBoost). CNNs capture spatial patterns for regional charging, while LSTMs model temporal dependencies in charging patterns. XGBoost adds efficiency by managing structured data and preventing overfitting through regularization. Dropout layers were introduced in the LSTM network to reduce overfitting. The deep charge-fusion model achieved an R² value of 0.81 for predicting fast-charging behaviors, outperforming standalone models such as gated recurrent unit (GRU)-based (R² = 0.51) and LSTM-based (R² = 0.43) models. It also achieved an R² of 0.81 for home charging predictions. These results underscore the role of socio-demographic factors and demonstrate that hybrid models significantly improve predictive accuracy, with implications for EV infrastructure planning and energy management strategies.

Publication metadata

Author(s): Cavus M, Ayan H, Bell M, Oyebamiji OK, Dissanayake D

Publication type: Article

Publication status: Published

Journal: International Journal of Transportation Science and Technology

Year: 2026

Volume: 22

Pages: 37-60

Print publication date: 01/06/2026

Online publication date: 07/03/2025

Acceptance date: 02/03/2025

Date deposited: 22/05/2025

ISSN (print): 2046-0430

ISSN (electronic): 2046-0449

Publisher: Elsevier

URL: https://doi.org/10.1016/j.ijtst.2025.03.002

DOI: 10.1016/j.ijtst.2025.03.002

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