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 authors investigated the effect of vehicle design, driver, and environment on the range prediction of electric vehicles (EVs) and identified future research and development directions in the EV research field, resulting in massive future and immediate EV penetration in the automotive market.

TL;DR: In this article , the authors identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks, and they posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue.

TL;DR: In this paper, the impact of massive integration of electric vehicles is analyzed, and the energy management tools of electric energy storage in EVs are provided, while the variety of services that EVs may provide is investigated.

TL;DR: In this paper, a more detailed study of the transboundary movements of e-waste is presented, which includes a systematic review of existing literature, the application of the Driver, Pressure, State, Impact, Response (DPSIR) framework for analysing complex problems associated with social ecological systems, and the evaluation of the Life Cycle Assessment (LCA) for evaluating the environmental impact of electronic devices from their manufacture through to their final disposal.