Inductive power transfer (IPT) presents a promising solution for opportunity charging of electric buses. However, achieving an optimal balance between pad area, power transfer efficiency, and misalignment tolerance remains a significant challenge. This article explores the tradeoffs between power transfer efficiency and area-related power density and investigates the electric field distribution in the charging pads of wireless charging systems. The design requirements are first established. Based on these, a multiobjective optimization (MOO) framework is developed to address insulation constraints and current density limitations within the windings. The resulting Pareto front reveals that lower area-related power densities correspond to reduced efficiency, highlighting a fundamental design tradeoff. Furthermore, the study identifies critical regions within the charging pads that are the most susceptible to insulation failure. A 50-kW prototype was implemented and tested, with experimental results showing a dc-dc power efficiency ranging from 97.165% to 96.824% under 100-mm X and Y misalignment, and a stray field of 13.86μ T.

Failures associated with thermo-mechanical fatigue are one of the dominant reasons for faults in power electronic converter-based electrical systems. This review explores such thermal stress-induced reliability challenges in power converters, focusing on key package-related failure mechanisms such as bond-wire fatigue, solder degradation, and chip metallization wear-out. The study emphasizes the importance of mission-profile-based reliability assessment, highlighting the effects of operational and environmental conditions on the long-term performance of power modules. Key findings reveal how repetitive thermal cycling and environmental variations lead to critical failures, underscoring the need for effective thermal management and design-for-reliability strategies. The primary goal of this paper is the quantitative, comparative reliability analysis across multiple high-power applications, moving beyond qualitative summaries. This review aims to support future research on predictive reliability modeling, mission-profile-based lifetime estimation, and robust design strategies for wide-bandgap-based high-power converters. Ultimately, the insights provided are intended to guide the development of more robust power electronic systems for emerging energy and mobility infrastructures.

Home Energy Management Systems (HEMS) play a key role in the electrification of the residential sector. In a future where different energy carriers must be converted and used seamlessly, HEMS are responsible for orchestrating all energy assets within a home. To determine the power flows, the HEMS must be aware of its impact on the aging of the system's batteries. On the other hand, since this is typically performed through optimization models, various variables are needed, such as ambient temperature, solar radiation, and initial conditions. These inputs are uncertain and require proper forecasting. This paper presents the operation of an aging-aware HEMS using probabilistic forecasts of exogenous information, directly relating them to the quality of the degradation control. The relationship is studied under deterministic and stochastic optimal control settings.

This paper presents a practical approach to reduce the size of medium-frequency, medium-voltage dry-type transformers through the innovative use of semiconductive screening. The proposed method minimizes the required air gaps, a critical aspect of dry-type transformer design, particularly for medium-frequency applications. Analytical approaches and Finite Element Method (FEM) simulations in COMSOL are used to demonstrate how to achieve a uniform electric field distribution within the transformers. Experimental investigations by means of partial discharge measurement on a prototype epoxy-based stress cone termination with a semiconductive shield are conducted. The results demonstrate the potential for this method to enhance transformer performance and provide a foundation for further advancements in medium-frequency transformer design.

Traction vehicles such as metro or trams are equivalent to power sources that move along traction grids with a low voltage-to-impedance ratio. This causes severe voltage drops that increase non-lineraly with the vehicle's position, power demand, and number of neighboring vehicles. As this power flow calculation sounds complex, it is often simplified by assuming that vehicles are current-sources. This significally reduces the complexity of the models and their simulation times at the potential expense of the accuracy. This work compares the error in modeling the vehicles as current-sources instead of power sources in two case studies of a 750V tram line of 6km in length. In the first case study, stochastic simulations with random vehicle position and power allows to investigate a wide array of scenarios. The errors are shown to be mostly significant for the minimum line voltage (up to 100V), transmission line losses (over 300kW), and substation power demand (over 400kW). For the second case study, where 4 hours of a timetable operation were used, errors up to 40% in the substation 15-min average power demand are reported. In general, the current-source modeling risks high errors in the computations of all traction-grid power flow variables. However, it shows acceptable outcome under specific situations such as the substation power demand under braking energy scenarios, even if that is where it shows the highest error for voltage calculations. This invites further investigations into hybrid current-power-source modeling approaches for certain variables that could save computational time by reducing the number of iterations.

Many DC energy solutions have emerged as potential candidates to enhance the electrical infrastructure in a localized approach, allowing future expansion in the transportation sector despite the congestion of the utility grid. However, the risk of designing large power converter units as controllable substations in complex networks, such as electric railway systems, has encouraged the sophistication of modeling and testing tools. This paper presents a high-fidelity, real-time model implementation of a controllable substation for DC traction power systems. This representative model is developed to facilitate the testing of different upgrading options to understand and quantify how these changes will affect the system and, more importantly, which features are critical to further increasing the sustainability of the railways. This is applied to a case study of the Dutch railway system in Wierden. It is found that while controllable substations can reduce voltage drops from an average of 400 V to only about 230 V, the benefit they bring in regenerative braking harvesting does not outweigh the investment costs, calling for further investigation of energy storage systems as another potential solution.

In dual active bridge (DAB) converters, the external series inductor is often placed on the high-voltage side to reduce its losses, but in this configuration, the transformer magnetizing inductance is excited by the reflected voltage of the low-voltage port. This configuration can lead to higher transformer core losses for the DAB converter. However, in a split inductor configuration, the magnetizing current is supplied by both the high-voltage and low-voltage side bridges, reducing the volt-seconds across the magnetizing inductance and therefore reducing core losses. In this work, an analytical expression for the transformer magnetization voltage is presented, and the reduction in transformer core loss achieved by using a split inductance configuration is calculated. An 11kW, 775V/450V prototype is implemented, and both magnetic configurations are experimentally compared under identical volume and thermal conditions for a wide power range at 450V. Under steady-state thermal conditions at 450V and 11kW, the split-inductance configuration achieves up to a 5.88% reduction in total converter losses and an 18.3°C decrease in the worst-case transformer core temperature compared to the high-voltage-side inductance configuration.

The operation of residential energy hubs with multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to different carrier dynamics, hybrid storage coordination and high-dimensional action-spaces. Energy management systems oversee their operation, deciding the set points of the primary control layer. This paper presents a novel 2-stage economic model predictive controller for electrified buildings including physics-based models of the battery degradation and thermal systems. The hierarchical control operates in the Dutch sequential energy markets. In particular common assumptions regarding intra-day markets (auction and continuous-time) are discussed as well as the coupling of the different storage systems. The best control policy it is best to follow continuous time intra-day in the summer and the intra-day auction in the winter. This sequential operation comes at the expense of increased battery degradation. Lastly, under our controller, the realized short-term flexibility of the thermal energy storage is marginal compared to the flexibility delivered by stationary battery pack and electric vehicles with bidirectional charging.

This article introduces the design of a high-bandwidth 7-level flying capacitor multi-level (FCML) converter for a hybrid reluctance actuator. The converter is designed with GaN MOSFETs. The hardware prototype managed to achieve THDs of -48 dB, a peak efficiency of 98.2 % and output powers up to 6 kW in open-loop testing.

As freight electrification progresses, the planning of high-power charging infrastructure becomes a critical challenge due to the interaction between time-constrained operations, spatially uneven demand, and limited grid-connection capacity. Current European Union regulation specifies corridor coverage requirements, but does not explicitly account for local demand intensity, station sizing, or grid constraints. This article presents an integrated optimization framework for planning highway charging infrastructure for heavy-duty electric vehicles (HDEVs). The framework combines freight-tour simulation, route- and energy-aware charging event generation, candidate-site identification, and optimization-based station siting, sizing, and event-level scheduling. In addition, a constant-current/constant-voltage (CC–CV) post-processing step is used to obtain more realistic charging durations and aggregate charging profiles. The framework is applied to the Netherlands for 2025, 2030, and 2040 under both demand-driven and regulatory-driven planning paradigms, considering charger ratings of 350 kW and 1 MW. The results show that demand-driven planning achieves full event feasibility on the modelled corridor network, whereas the regulatory-driven planning exhibits a small but persistent feasibility gap due to its more geographically constrained candidate set. Across scenario years, infrastructure expansion follows a two stage pattern, shifting from initial spatial coverage to capacity densification at persistent freight corridors. Sensitivity analysis further shows that 1 MW networks are substantially more exposed than 350 kW networks to grid limits and vehicle-side charging assumptions. Overall, the proposed framework provides a practical decision support tool for designing scalable, cost-effective, and operationally realistic HDEVs charging networks.

This study presents a current balancing technique for high-current windings in medium-frequency transformers (MFTs), particularly relevant to solid-state transformer (SST) applications. Handling high currents on the low-voltage high-current winding of MFTs is challenging due to skin and proximity effects. Conventional techniques, such as continuously transposed conductors (CTCs) and parallel winding paths, are applicable but have limitations in medium-and high-frequency applications such as SSTs due to skin and proximity effects. To address these issues, a modular and tunable compensation method is proposed, based on adding small, series-connected inductive elements (compensation toroids) to each parallel winding path. Experimental results from a prototype validate the proposed compensation technique, highlighting its effectiveness in mitigating unbalanced current distribution. Finite element analysis (FEA) and experimental validation across a wide frequency range (1–10 kHz) confirm the effectiveness of the method. The results demonstrate a significant reduction in current imbalance with minimal added losses or system impact.

Current source inverters (CSIs) have attracted increasing attention for motor drive applications owing to their inherent short-circuit protection and motor-friendly characteristics. However, conventional CSI-fed drive systems typically require multiple sensors and a bulky DC-link inductance, hindering reliable and compact system designs. This article proposes a prediction-based coordinated control strategy for the front-end and rear-end stages of an H8-CSI-fed high-speed permanent magnet synchronous motor drive, enabling a compact CSI system that achieves reduced sensor count and low DC-link inductance without compromising the dynamic performance. For the rear-end CSI and motor control, the predictive current control and the rotor-position sensorless algorithm are implemented using only motor-current measurements, thereby eliminating both the capacitor-voltage and rotor-position sensors. In the front-end, multistep load-power prediction from the rear-end is incorporated into the current controller design to enable stable operation under low DC-link inductance. Experimental results verify that the proposed method maintains stable operation under a 10 A DC-link current with a 136 μH DC-link inductance, achieving q-axis current step responses within three control steps at approximately 1 kHz electrical frequency. Comparative experiments against representative state-of-the-art multi-state-feedback CSI control methods further validate that the proposed method achieves faster current dynamic response with fewer sensors and a smaller DC-link inductance.

This paper presents a sequential framework based on a two-stage stochastic programming model for the planning and expansion of distribution networks under the influence of heavy-duty electric vehicles (HDEVs) charging. In the first step, a capacity assignment model determines the spatio-temporal charging demand of HDEVs, where a congestion penalty function is introduced to produce spatially diversified charging demand, preventing vehicle clustering. In the second step, this demand is used as input to a mixed-integer second-order cone programming (MISOCP) model for the joint planning of energy sources and grid reinforcements, which is solved via Benders decomposition. The objective is to minimize total asset investment and operational costs while accounting for uncertainties in electricity demand, renewable energy generation and the arrival of HDEVs at charging stations. The framework is tested across fleet electrification rates and seasonal PV generation conditions to quantify how grid investment scales with HDEV adoption.

In the context of building electrification, the operation of distributed energy resources integrating multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to the nonlinear device dynamics, uncertainty, and computational issues. As such, energy management systems seek to decide the power dispatch in the best way possible. The objective is to minimize and balance operative costs (energy bills or asset degradation) with user requirements (mobility, heating, etc.). Current energy management uses empirical battery ageing models outside of their specific fitting conditions, resulting in inaccuracies and poor performance. Moreover, the link to thermal systems is also overlooked. This paper presents an ageing-aware nonlinear economic model predictive controller for electrified buildings that incorporates physics-based battery ageing models. The models distinguish between energy storage systems (chemistry, ageing state, etc.) and make explicit the trade-off between grid cost and battery degradation. The proposed algorithm can either cut down on grid costs or extend battery lifetime (electric vehicle or stationary battery packs). Additionally, substituting NMC cells with LFP chemistries optimizes grid performance during the summer, yielding a 10% grid cost reduction and a 20% decrease in degradation. Finally, the grid cost and degradation of the presented MPC when using aged batteries are improved with respect to the state of the art by 10% and 5% respectively, in periods with high solar generation and low thermal loads like summer.

The greater reach of electrification has forced modernization in all modes of transportation. Electric railway systems have historically been at the forefront of electrification, currently being among the most efficient options. However, to fully embrace sustainability, further improvements and modernizations are necessary to diversify the energy supply and enhance grid functionality. This paper presents a modernization approach for DC electric railway systems (ERS) that minimizes intervention and adopts a modular design. This strategy is based on a cost-effective approach to deploying the concept of a Solid-State Transformer, specifically a modularized bridge rectifier. This unidirectional structure can utilize additional isolated stages to feed an auxiliary load, allowing its supply to be made in a manner that enhances power quality at the AC side. This paper applies this emerging concept to improve the performance of the passive front ends typically employed in the DC Railways substations, aiming to improve the power quality, reduce the peak power consumption during high-congestion scenarios and more importantly, evaluate a cost-effective, scalable, and modular approach to modernizing a system that cannot afford a green field approach for embracing a sustainable diversification of its energy sources. The concept is introduced and analyzed theoretically, and simulations are conducted in PLECS for various scenarios based on the Dutch ERS.