Electric vehicle charging technologies, infrastructure expansion, grid integration strategies, and their role in promoting sustainable e-mobility

Abstract: The emergence of electric vehicles (EVs) as key elements in the decarbonization of transportation demands a new class of intelligent infrastructure capable of optimizing charging behavior while maintaining power system stability. This paper proposes a novel Scalable Cloud-Based Continuous Monitoring Platform (SC-CMP) designed to support real-time optimization of microgrid operations, particularly in EV-dense and renewable-integrated environments. By fusing cloud computing, machine learning (ML), and artificial intelligence (AI) with Internet of Things (IoT) data acquisition, SC-CMP enables continuous monitoring, predictive scheduling, and adaptive energy management across distributed power networks. Unlike conventional systems, SC-CMP supports both centralized and decentralized microgrid architectures, providing scalable support for dynamic load balancing, V2G coordination, and resilient energy dispatch. Simulation and validation are performed using a real-world dataset of 3395 EV charging sessions across 105 stations, demonstrating SC-CMP's superiority over existing AI/ML baselines. Quantitatively, the platform achieves 97.34% predictive accuracy, 96.81% grid stability improvement, 94.5% resource allocation efficiency, 93% scalability, and 95.2% data privacy assurance. These outcomes position SC-CMP as a comprehensive, adaptive, and cost-effective solution for microgrid-oriented EV integration, offering substantial advances in resilient power distribution, renewable energy utilization, and sustainable electric mobility. The platform serves as a foundation for next-generation microgrid control systems that demand real-time intelligence, scalability, and reliability across evolving smart grid landscapes.

TL;DR: This study integrates S-curve modeling and social network analysis to examine electric vehicle patent trends globally and in South Korea, identifying saturation, sustainable research directions, and technological advantages, guiding stakeholders in EV technology development.

TL;DR: In this article, the potential impact of EV charging on distribution transformers at residential level is analyzed based on the load consumption in East Texas and different assumptions of EVs charging scenarios, the impact is demonstrated and compared on monthly basis.

TL;DR: This global review identifies contextual, situational, demographic, and psychological factors influencing EV adoption, with situational factors being most influential, citing lack of charging stations and limited driving range as barriers and air pollution reduction and policy incentives as motivators.

TL;DR: In this paper , the authors identified 23 potential enablers through literature review and discussion with domain experts, and analyzed the cause-effect relationship using a hybrid grey-based decision-making trial and evaluation laboratory approach.