Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction

Meriem Manai, Bassem Sellami, Sadok Ben Yahia

2024

Abstract

The electric vehicle (EV) market is experiencing substantial growth, and it is anticipated to play a major role as a replacement for fossil fuel-powered vehicles in transportation automation systems. Nevertheless, as a rule of thumb, EVs depend on electric charges, where appropriate usage, charging, and energy management are vital requirements. Examining the work that was done before gave us a reason and a basis for making a system that forecasts the real-time availability of electric vehicle charging stations that uses a scalable prediction engine built into a server-side software application that can be used by many people. The implementation process involved scraping data from various sources, creating datasets, and applying feature engineering to the data model. We then applied fundamental models of machine learning to the pre-processed dataset, and subsequently, we proceeded to construct and train an artificial neural network model as the prediction engine. Notably, the results of our research demonstrate that, in terms of precision, recall, and F1-scores, our approach surpasses existing solutions in the literature. These findings underscore the significance of our approach in enhancing the efficiency and usability of EVs, thereby significantly contributing to the acceleration of their adoption in the transportation sector.

Paper Citation

in Harvard Style

Manai M., Sellami B. and Ben Yahia S. (2024). Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 351-358. DOI: 10.5220/0012752600003753

in Bibtex Style

@conference{icsoft24,
author={Meriem Manai and Bassem Sellami and Sadok Ben Yahia},
title={Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={351-358},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012752600003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction
SN - 978-989-758-706-1
AU - Manai M.
AU - Sellami B.
AU - Ben Yahia S.
PY - 2024
SP - 351
EP - 358
DO - 10.5220/0012752600003753
PB - SciTePress