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.

This paper intends to establish an overall up-to-date review on Fast Charging methods for Battery Electric Vehicles (BEV). This study starts from basic concepts involving single battery cell charging, current and future charging standards. Then, some popular power converter topologies employed for this application are introduced, and finally a summary of the industrial solutions available on the market are presented, as well as the ongoing projects related to the extreme fast charging (XFC) network expansion. Practical insights, considering the current BEV scenario, are employed to get a better understanding of this topic. Special attention is given to the modular design approach, analyzing its advantages and some of the factors that influence the number and size of modules that conform a fast charger solution

Interleaved dc-dc converters with integrated magnetic components capable to achieve high power density have recently gained attention in automotive applications for eco-friendly transportation. These circuits may contribute to the downsizing of powertrains for electric vehicles (EVs) and hybrid electric vehicles (HEVs) using close-coupled inductors (CCIs) and loosely coupled inductors (LCIs). In this study, a novel core model for size and loss calculation of the interleaved converter with integrated winding coupled inductors (IWCIs) is proposed to assess the size of magnetic components. This assessment was carried out using the parameters of a 1-kW circuit. As a result, interleaved converters with IWCIs proved to be more effective than the converters with LCIs and CCIs for downsizing the magnetic components. In contrast, it was also found that the interleaved converter with LCIs achieved downsizing of the magnetic component at a duty ratio close to 50%. Finally, the effectiveness of the calculation model was validated through experimental tests.

The paper provides an overview of how Artificial Intelligence (AI) is applied in designing highfrequency 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. Firstly, 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, the paper highlights significant advancements: the development of AI-driven models for precise loss estimation in highfrequency 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 paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.