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The focus of this paper is to conduct a methodical review regarding the use of digital twins in the powertrain of electric vehicles (EVs). While reviewing the development of digital twin technology, its main application scenarios and its use in electric vehicle powertrains are analysed. Finally, the digital twins currently encounter several challenges that need to be addressed, and so the future development of their application to electric vehicles are summarized.

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 study employs the ELECTRE (ELimination Et Choix Traduisant la REalité - ELimination and Choice Expressing the REality) TRI-nC method to classify 27 EU Member States (MSs) regarding their governance in terms of EV technology promotion. Overall, financial incentives still have a big effect on EV deployment, since those countries with greater concern on this topic were generally better classified than the rest. Finally, charging infrastructures also play a critical role, either making or breaking the deployment of EVs, leading to the worst classification of MSs with very few charging points per 100 thousand urban inhabitants.

This study explores consumer preferences and acceptance of EV user interfaces (UIs), focusing on expectations, perceptions and preferences of aesthetics, design, function, and features. The work presented here is part of a larger project investigating EV adoption in the Australian market and presents a qualitative observation of participants interacting with EVs.

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 explores the secondary use of batteries in stationary energy storage systems (B2U storage systems) proposed for the circularity of electromobility. To implement such systems, a circular business model and a cross-industry ecosystem are required. However, the meaning, scope, and structure of these concepts have received little research to date. To close this gap, a theoretical construct for a circular business model based on the theory of business model, sustainability, circular economy, and ecosystem must be developed.

Multilevelπ π-type and ANPC converters without flying capacitors and clamping diodes are emerging candidates for industrial applications due to their simple structure, less number of devices, and better harmonic performance. However, the voltage balancing difficulty is the key issue of these topologies similar to diode-clamped topology under conventional PWM methods. The unbalance of capacitors voltages may affect the system integrity and stability, and may degrade harmonic performance. To sort out this issue, a simplified virtual vector PWM method to balance the dc-link capacitors voltage of four-level three-phase π -type and ANPC converters is presented in this paper. Simulation results show how easily and efficiently the proposed method can control the voltage of the capacitors for different modulation index values under balanced and unbalanced loads.

This study identifies and examines circular scenarios within the permanent magnet motor industry and provides planning strategies for PM motor recycling. The study adopted a two-stage research design, including exploration and evaluation phases and a SWOT analysis. Based on the findings, the study develops strategic strategies to help automotive companies transition to a circular economy.

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.