Urban areas account for a significant share of global energy consumption and CO₂ emissions, making the shift to sustainable transport both urgent and complex. The eMob project addresses this challenge by developing intelligent ecological driving strategies for both hybrid and fully electric vehicles. Through the use of artificial intelligence and geofencing, eMob seeks to optimize each vehicle’s energy usage in real time, thus reducing tailpipe emissions and operational costs without sacrificing performance.

Building upon previous research (iSUN) in plug-in hybrid buses, the project extends its scope to private cars, heavy-duty trucks, and connected autonomous buses. A key aspect is the design of zero-emission corridors and zone-based propulsion strategies, ensuring the most effective use of electric power when traveling through designated areas. Additionally, eMob employs multifractal analysis to measure environmental impacts, providing a scientifically robust metric for evaluating improvements in air quality and energy efficiency.
By integrating accurate traffic simulations, machine learning algorithms, and high-level optimization, eMob delivers versatile solutions adaptable to current and emerging vehicle technologies. Ultimately, this project aims to advance the transition to cleaner, more efficient urban and interurban transport, paving the way for future innovations in mobility and sustainable city planning.

In order to be able to carry on these research activities a multidisciplinary team have been formed. Some of them are part of GOAL research group at Universidad de Cádiz.Acknowledgements: This project [PID2022-137858OB-I00] has been supported by Spanish national call Transición Ecológica y Digital  funded by MCIN/AEI10.13039/ 501100011033 and the European Union NextGenerationEU/PRTR”.