Gallium nitride high-electron-mobility transistor (GaN HEMT) is a key enabling technology for obtaining high-efficient and compact power electronic systems. At the design stage of a power converter, the proper modelling of the GaN HEMT is essential to benefit from their good features and to account for the limits of the current technology. Circuit models of power MOSFETs have been deeply investigated by academia and industry for a long time. These models are able to emulate the datasheet information, and they are usually provided by device manufacturers as netlists that can be simulated in any kind of SPICE-like software. This paper firstly highlights the similarities and differences between MOSFETs and GaN HEMTs at the datasheet level. According to this analysis, the features of MOSFET circuit models that can be adopted for GaN HEMT modelling are discussed. This task has been accomplished by overviewing the literature on MOSFETs circuit models as well as analysing manufacturers netlists, thus highlighting the models MOSFETs valid or adaptable to GaN HEMTs. The study has revealed show that some models can be adapted for the GaN HEMT devices to emulate static characteristics at room temperature while the MOSFET models of dynamic characteristics can be used for GaN HEMT devices. This study enables the devices modellers to speed up the GaN HEMT modelling thanks to the use of some well-established MOSFET models. In this perspective, some suggestions to develop accurate GaN HEMT models are also provided.

High switching frequencies in power converters allow to use small-size passive components that, in turn, means high-density (kW/kg) power conversion. The use of silicon MOSFETs manufactured with super junction technology is an additional feature towards the achievement of very high-density converters. The concurrent factors of increased switching frequency and power density may lead to thermal issues to be faced. Aiming to reduce the overall power losses, soft switching techniques are adopted to reduce dramatically the switching power losses, thus increasing the efficiency. The application of super junction (SJ) MOSFETs in soft-switching based conversion schemes has revealed unexpected power losses due to the dielectric hysteresis phenomenon into the parasitic output capacitance Coss of the MOSFET. In this respect, the experimental evidence has proved that there is a very large performance difference among the devices available into the market. This paper studies the Coss power losses that have been experimentally measured, modeled and simulated by a PSpice package. The proposed model is a nonlinear resistor whose average value is equal to the equivalent series resistance calculated from the experimental hysteresis loop (Q- V characteristic) and the related power losses at a given switching frequency.

In multi‐ and many‐objective optimization problems, the optimization target is to obtain a set of non‐dominated solutions close to the Pareto‐optimal front, well‐distributed, maximally extended and fully filled. Comparing solution sets is crucial in evaluating the performance of different optimization algorithms. The use of performance indicators is common in comparing those sets and, subsequently, optimization algorithms. Therefore, an effective performance indicator must encompass these features as a whole and, above all, it must be Pareto dominance compliant. Unfortunately, some of the known indicators often fail to properly reflect the quality of a solution set or cost a lot to compute. This paper demonstrates that the Degree of Approximation (DOA) quality indicator is a weakly Pareto compliant unary indicator that gives a good estimation of the match between the approximated front and the Pareto‐optimal front.

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.