The growing penetration of Electric Vehicles (EVs) in power distribution networks presents both challenges and opportunities for grid operators and planners. This paper provides a comprehensive review of recent advances in optimization techniques and machine learning (ML) approaches for the efficient management of EV charging and integration in low- and medium-voltage distribution systems. Optimization methods are analyzed with reference to their objectives—such as load flattening, voltage regulation, loss minimization, and infrastructure cost reduction—and their capability to handle multi-objective, stochastic, and real-time constraints. Concurrently, the role of ML is explored in load forecasting, user behavior modeling, anomaly detection, and adaptive control strategies. Particular attention is given to hybrid approaches that combine optimization algorithms (e.g., MILP, heuristic methods) with data-driven models (e.g., neural networks, reinforcement learning), highlighting their effectiveness in enhancing grid flexibility and resilience. This review adopts a unified system-level perspective that links EV management objectives, optimization techniques, and machine learning-based solutions within distribution networks. In addition, particular attention is devoted to data availability, reproducibility, and practical deployment aspects, with the aim of identifying current limitations and providing actionable insights for future research and real-world applications. This study aims to support the development of intelligent energy management strategies for EVs, fostering a sustainable and reliable evolution of distribution networks.

The hybrid FEM-DBCI numerical method is proposed for the computation of coupling factors between time-harmonic magnetic fields and human bodies. Characteristics are highlighted which make FEM-DBCI very suitable to perform such computations. Several coil geometries are considered (circular coils) at low frequency. A simplified model of the human body is assumed as suggested by IEC standards. Details of the method are highlighted, and numerical results are also provided.

This paper presents a novel technique for controlling the Power Factor Correction (PFC) of a two-stage converter. The proposed solution operates the PFC in a special intermittent mode at a medium or light load. As a result, the flyback converter stage can be optimized to operate within a tight input voltage range, thus obtaining better efficiency and more compactness compared to a traditionally controlled two-stage converter. Circuit models of the converter have been developed to test the goodness of the proposed solution.