Strategic Approach for Electric Vehicle Charging Infrastructure for Efficient Mobility along Highways: A Real Case Study in Spain

Abstract

The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip.

Comparative Analysis of Sustainable Electrification in Mediterranean Public Transportation

“…Electric-powered public transportation has proven effective in alleviating congestion and reducing pollution in urban settings [10]. Although other clean technologies exist, such as hydrogen and alternative fuel-powered engines, electrification remains the most practical approach to combat urban air pollution [11,12]. This shift involves the displacement of internal combustion engines in favor of electricitypowered counterparts, encompassing battery-equipped electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs), collectively referred to as EVs [7].…”

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

“…In detail, ANN is often developed to evaluate Li-ion battery performance. The accuracy of the evaluation process on battery voltage and SoC was discovered to be more reliable during the discharging phase than charging operations [105]. In addition, the parallel estimation performed in cooperation between ANN and physicsbased models such as ECM shows an improvement in accuracy, with a reduced root mean squared error (<1%) and R 2 (~0.96-0.995) [40,106].…”