Decision-Making System for Electric Vehicle Management by Integrating Smart Technologies and Local Characteristics
Kunicina N. Beliaev V. Grants R. Caiko J. Amanova R. Brūzgienė R. Mansurova M.
December 2024Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2024#14Issue 23
With the global shift to electric vehicles, countries face unique challenges and opportunities shaped by their geographical and economic contexts. This paper presents a system that leverages smart transport technologies, the Internet of Things, and decision-making algorithms, such as PROMETHEE, to optimize charging stations and their positioning in diverse urban and rural settings. The system addresses key obstacles, including managing charging infrastructure, balancing energy consumption, and enhancing transport accessibility. By analyzing local conditions, the proposed solution incorporates innovative algorithms for electricity demand forecasting, charging station management, and integration with urban transport systems. This approach ensures a flexible, scalable, and sustainable electric vehicle management system that aligns with international standards and evolving technological trends.
automation of processes , decision support system , electric vehicle , intelligent control system , machine learning remote control , MCDM , power supply , process monitoring , process optimization
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Faculty of Computer Science, Information Technology and Energy, Institute of Industrial Electronics and Electrical Engineering, Riga Technical University, LV, Riga, 1048, Latvia
Faculty of Power and Electrical Engineering, Institute of Industrial Electronics and Electrical Engineering, Riga Technical University, LV, Riga, 1048, Latvia
Artificial Intelligence and Big Data Group, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Computer Sciences, Faculty of Informatics, Kaunas University of Technology, Kaunas, 44249, Lithuania
Department of Artificial Intelligence and Big Data, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Faculty of Computer Science
Faculty of Power and Electrical Engineering
Artificial Intelligence and Big Data Group
Department of Computer Sciences
Department of Artificial Intelligence and Big Data
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