• Energy Research

The integration of Full Electric Vehicles (FEVs) into the smart city ecosystem is an essential step towards achieving sustainable urban mobility. This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. The model integrates advanced components such as a Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamic user interface. Important aspects include analyzing power consumption, forecasting urban energy demand, and monitoring the State of Charge (SoC) of FEV batteries using innovative algorithms validated through real-world applications in Valencia (Spain) and Ljubljana (Slovenia). Results indicate high accuracies in SoC tracking (error < 0.05%) and energy demand forecasting (MSE ~6 × 10−4), demonstrating the model’s reliability and adaptability across diverse urban environments. This research contributes to the development of resilient, efficient, and sustainable smart city frameworks, emphasizing real-time data-driven decision-making in energy and mobility management.

In this paper, we propose a novel demand-side management (DSM) system designed to optimize electric vehicle (EV) charging at public stations using model predictive control (MPC). The system adjusts to real-time grid conditions, electricity prices, and user preferences, providing a dynamic approach to energy distribution in smart city infrastructures. The key focus of the study is on reducing peak loads and enhancing grid stability, while minimizing charging costs for end users. Simulations were conducted under various scenarios, demonstrating the effectiveness of the proposed system in mitigating peak demand and optimizing energy use. Additionally, the system’s flexibility enables the adjustment of charging schedules to meet both grid requirements and user needs, making it a scalable solution for smart city development. However, current limitations include the assumption of uniform tariffs and the absence of renewable energy considerations, both of which are critical in real-world applications. Future research will focus on addressing these issues, improving scalability, and integrating renewable energy sources. The proposed framework represents a significant step towards efficient energy management in urban settings, contributing to both cost savings and environmental sustainability.