View in EDS

Electric vehicles charging station allocation based on load profile forecasting and Dijkstra's algorithm for optimal path planning.

Saved in:
Bibliographic Details
Title: Electric vehicles charging station allocation based on load profile forecasting and Dijkstra's algorithm for optimal path planning.
Authors: Boubaker, Sahbi, Al-Dahidi, Sameer, Kamel, Souad, Ghazouani, Nejib, Kraiem, Habib, Alsubaei, Faisal S., Bourennani, Farid, Meskine, Walid, Benghanem, Mohamed, Mellit, Adel
Source: Scientific Reports; 7/4/2025, Vol. 15 Issue 1, p1-23, 23p
Subject Terms: ELECTRIC vehicle charging stations, ARTIFICIAL intelligence, ELECTRIC vehicle industry, EDGE computing, IMAGE processing, ELECTRIC charge
Abstract: The widespread adoption of Electric Vehicles (EVs) presents new challenges for efficient and timely access to Charging Stations (CSs), particularly under constraints of limited availability and variable demand. The current investigation addresses the EV charging station allocation problem, aiming to guide EVs to optimal CSs based on real-time and forecasted system dynamics. An integrated framework that combines load profile forecasting, optimal path planning, and drone-assisted edge computing is proposed to support decision-making. Specifically, a Nonlinear Auto-Regressive with Exogenous inputs (NARX) model is used to predict future load profiles at CSs, enabling proactive management of charging demand. To determine the most accessible stations, Dijkstra's algorithm for shortest-path computation based on the EV's current location and the locations of the CSs around is applied. Furthermore, drones with lightweight edge computing algorithms enabled real-time data exchange between CSs and EVs, providing up-to-date information on slot availability and local crowd conditions. For the forecasting component, the NARX model has provided a correlation coefficient of 90% for the CS real data collection. Dijkstra's algorithm was employed to effectively optimize the routing of EVs to their nearest charging stations by determining optimal shortest paths. The simulation results demonstrate that the proposed approach significantly enhances EV allocation efficiency while reducing both waiting times and travel distances. Further research is needed to address regulatory and logistical challenges associated with drone deployment in real-time applications. [ABSTRACT FROM AUTHOR]
Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:The widespread adoption of Electric Vehicles (EVs) presents new challenges for efficient and timely access to Charging Stations (CSs), particularly under constraints of limited availability and variable demand. The current investigation addresses the EV charging station allocation problem, aiming to guide EVs to optimal CSs based on real-time and forecasted system dynamics. An integrated framework that combines load profile forecasting, optimal path planning, and drone-assisted edge computing is proposed to support decision-making. Specifically, a Nonlinear Auto-Regressive with Exogenous inputs (NARX) model is used to predict future load profiles at CSs, enabling proactive management of charging demand. To determine the most accessible stations, Dijkstra's algorithm for shortest-path computation based on the EV's current location and the locations of the CSs around is applied. Furthermore, drones with lightweight edge computing algorithms enabled real-time data exchange between CSs and EVs, providing up-to-date information on slot availability and local crowd conditions. For the forecasting component, the NARX model has provided a correlation coefficient of 90% for the CS real data collection. Dijkstra's algorithm was employed to effectively optimize the routing of EVs to their nearest charging stations by determining optimal shortest paths. The simulation results demonstrate that the proposed approach significantly enhances EV allocation efficiency while reducing both waiting times and travel distances. Further research is needed to address regulatory and logistical challenges associated with drone deployment in real-time applications. [ABSTRACT FROM AUTHOR]
ISSN:20452322
DOI:10.1038/s41598-025-08840-3