Artificial Intelligence-Driven Optimization of Electric Vehicle Charging Networks: An Integrated Framework for Sustainable Supply Chain Operations
Year: 2025
Type: article
Abstract: This article presents an innovative framework for optimizing electric vehicle charging networks in supply chain operations through artificial intelligence-driven solutions. The article addresses critical challenges in EV fleet management by integrating advanced neural networks, blockchain technology, and Internet of Things architecture to create a comprehensive charging optimization system. The framework incorporates multi-agent learning algorithms, real-time data processing, and smart grid integration to enhance operational efficiency and sustainability. Through extensive experimental validation and real-world implementations, the article demonstrates significant improvements in charging optimization, energy cost reduction, and environmental impact mitigation. The proposed system leverages sophisticated sensor networks, edge computing capabilities, and dynamic pricing mechanisms to achieve superior performance compared to traditional charging management approaches. This article contributes to the advancement of sustainable transportation infrastructure by developing practical solutions that can be implemented across diverse operational environments while maintaining high reliability and user satisfaction levels.
Author: Abdul Muqtadir Mohammed
Institution: University at Buffalo, State University of New York
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407354635