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This article tackles the complex challenge of predicting magnetic iron losses in high-frequency magnetic components by introducing a novel conditional generative adversarial network model. Diverging from traditional loss prediction methodologies that often overlook intricate interactions of factors, our conditional generative adversarial network framework is designed to comprehensively incorporate diverse aspects such as material properties, geometrical variations, and environmental conditions. To facilitate this advanced approach, a specialized four-wire measurement kit was employed, which significantly enriched the training dataset with a wide range of measurements. When benchmarked against conventional deep neural network models, the conditional generative adversarial network not only achieves faster convergence but also demonstrates markedly superior accuracy in predicting iron losses. This superiority is particularly notable in scenarios that extend beyond the training data's range, underscoring the model's robustness and adaptability. Such advancements in predictive accuracy and efficiency represent a significant leap forward in the design and optimization of high-frequency magnetic components.

This article provides an overview of how artificial intelligence (AI) is applied in designing high-frequency magnetic components, primarily high-frequency inductors and transformers, for power electronics systems. Four categories of AI, including expert systems, fuzzy logic, metaheuristic methods, and machine learning techniques, are addressed. First, AI models for estimating losses in high-frequency magnetic components are discussed. Subsequently, AI-based design methods in high-frequency inductors and transformers are observed. Then, AI tools applied to the automatic design of high-frequency magnetic components are introduced and compared. Drawing insights from an analysis of over 200 publications, this article highlights significant advancements: the development of AI-driven models for precise loss estimation in high-frequency magnetic components, the application of AI in optimizing design configurations for the components, and the automation of design processes. These achievements demonstrate AI's capability to enhance the efficiency, performance, and innovation in high-frequency magnetic component design, offering a roadmap for future research in power electronics systems.

This article deals with the electrothermal characterization of multi-turn windings in the magnetic components of power electronics (PEs) converters (inductors and transformers). The ever higher frequencies and smaller PE components demand the fast and accurate power loss computation for an optimal design accounting for severe electrothermal constraints. Homogenization techniques prove indispensable. Works on both electromagnetic and thermal homogenization exist but mainly consider both physics independently. We aim at studying the interaction of the strongly coupled systems when applying homogenization techniques. As test case, we consider a 2-D EI-planar inductor with two different multi-turn copper windings.

Electric vehicles (EVs) are vital in the transition toward a sustainable and carbon-neutral future. However, the widespread adoption of EVs currently depends on the convenience of the charging process and the availability of their charging infrastructure. Consequently, onboard chargers (OBCs), offering an ac-charging solution built into most EVs, have gained significant attention. Furthermore, bidirectional OBCs enable reverse power flow, whereby the EV battery can be used to power various devices, homes, or even the electric grid. However, as the trend towards bidirectional OBCs becomes evident, new power converter design challenges arise, intensifying the need for high-efficiency, compact and cost-competitive solutions. This article extensively reviews the state-of-the-art bidirectional on-board chargers by analyzing over 500 publications, identifying the key trends, challenges, and research opportunities that will influence the development of next-generation bidirectional OBCs. Hence, various strategies to achieve cutting-edge performance are deducted. This includes the rise of high-voltage batteries, the integration of powertrains, the growing adoption of wide-bandgap semiconductors, and the use of integrated planar magnetic components, all aiming to enhance efficiency and power density. This article is accompanied by a CSV file recording all pertinent references to support future research, statistical analysis, and other contributions.

A comprehensive description of magnetically controlled transformers is presented in this article to assess their potential in isolated and resonant power converters. These converters benefit from implementing variable magnetics as the soft switching zones and voltage operating ranges are extended, along with fixed- or quasi-fixed-frequency operation. To represent the variation of the transformer's equivalent turns ratio and parallel–series inductance, the cantilever model is selected. Moreover, two novel transformer structures employing parallel premagnetization are introduced; only off-the-shelf magnetic cores are required for their implementation. In conjunction with small-signal analytical expressions, an adapted SPICE model is provided and compared to finite-element analysis (FEA) simulations. Additionally, the impact of the stray inductance of the structures is assessed, improving the understanding of this controllable magnetic device's constraints and tradeoffs. Experimental results align with the derived models and prove the proposed structures to provide significant variations in transformer equivalent turns ratio, keeping a relatively high parallel–series inductance ratio.