Efficient Charging Station Selection for Minimizing Total Travel Time of Electric Vehicles

Yaqoob Al-Zuhairi (), Prashanth Kannan, Alberto Bazán Guillén, Luis J. de la Cruz Llopis and Mónica Aguilar Igartua ()
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Yaqoob Al-Zuhairi: Department of Network Engineering, Universitat Politècnica de Catalunya (UPC), Barcelona 08034, Spain
Prashanth Kannan: Department of Network Engineering, Universitat Politècnica de Catalunya (UPC), Barcelona 08034, Spain
Alberto Bazán Guillén: Department of Network Engineering, Universitat Politècnica de Catalunya (UPC), Barcelona 08034, Spain
Luis J. de la Cruz Llopis: Department of Network Engineering, Universitat Politècnica de Catalunya (UPC), Barcelona 08034, Spain
Mónica Aguilar Igartua: Department of Network Engineering, Universitat Politècnica de Catalunya (UPC), Barcelona 08034, Spain

Future Internet, 2025, vol. 17, issue 8, 1-39

Abstract: Electric vehicles (EVs) have gained significant attention in recent decades for their environmental benefits. However, their widespread adoption poses challenges due to limited charging infrastructure and long charging times, often resulting in underutilized charging stations (CSs) and unnecessary queues that complicate travel planning. Therefore, selecting the appropriate CS is essential for minimizing the total travel time of EVs, as it depends on both driving time and the required charging duration. This selection process requires estimating the energy required to reach each candidate CS and then continue to the destination, while also checking if the EV’s battery level is sufficient for a direct trip. To address this gap, we propose an integrated platform that leverages two ensemble machine learning models: Bi-LSTM + XGBoost to predict energy consumption, and FFNN + XGBoost for identifying the most suitable CS by considering required energy, waiting time at CS, charging speed, and driving time based on varying traffic conditions. This integration forms the core novelty of our system to optimize CS selection to minimize the total trip duration. This approach was validated with SUMO simulations and OpenStreetMap data, demonstrating a mean absolute error (MAE) ranging from 2.29 to 4.5 min, depending on traffic conditions, outperforming conventional approaches that rely on SUMO functions and mathematical calculations, which typically yielded MAEs between 5.1 and 10 min. These findings highlight the proposed system’s effectiveness in reducing total travel time, improving charging infrastructure utilization, and enhancing the overall experience for EV drivers.

Keywords: electric vehicles; optimal charging station; bidirectional long short-term memory; feed-forward neural network; XGBoost regressor; realistic urban scenarios (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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