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 research presents a neural-adaptive control technique for DC microgrids in renewable hydrogen production systems. The proposed approach tackles voltage stability issues arising from variable solar production and fluctuating electrolyzer loads with an adaptive neural network-based proportional-integral (ANN-PI) controller with online system identification. The control architecture utilizes two concurrent multilayer perceptron (MLP) networks: one for real-time system identification to estimate the Jacobian matrix, and another for adaptive proportional-integral (PI) parameter adjustment. The decentralized architecture removes communication dependencies among converters, hence improving reliability and scalability. Simulation results indicate a better dynamic response with a 50% decrease in settling time, increased voltage stability retaining the DC bus voltage within ±2% of the nominal 400 V, and resilient performance under diverse situations, including load transitions and changes in solar irradiation. The neural-adaptive method effectively facilitates intelligent, model-free regulation for electric-hydrogen DC microgrids.

Accurate battery capacity estimation is essential for the effective and reliable operation of lithium-ion battery management systems. Battery impedance is a key parameter that encapsulates electrochemical information, closely correlating with the internal states of batteries. This study proposes a novel capacity estimation framework that effectively balances accuracy, efficiency, and practicality. Firstly, a novel feature extraction method is introduced to extract health features from the imaginary impedance at a single frequency. The extracted feature demonstrates a strong and stable correlation with battery degradation under various operation conditions, while significantly reducing data requirements. To address the impact of diverse degradation patterns on estimation accuracy, an initial adjustment method is applied to precisely retrace the relative degradation paths of batteries. The results show that the mean absolute percentage error of battery capacity estimation decreases from 15.65% to 2.87%. Additionally, a transformer-based capacity estimation model is developed, which integrates a feature fusion unit to explicitly eliminate the influence of temperature on model performance. As a result, the model's accuracy improves by over 28% under various thermal conditions.

This article presents a dual-side capacitor tuning and cooperative control strategy for wireless electric vehicle (EV) charging. To improve the efficiency of wireless EV charging across broad output voltages and wide-range load variations, this article introduces a reconfigurable WPT system by incorporating two switch-controlled-capacitors (SCCs) into the double-sided LCC (DLCC) compensation network. Based on the analytical model of the system, optimal capacitor tuning factors are derived to reduce the rms values of the inductor currents and to minimize the turn-off currents across the semiconductors. Furthermore, a dual-side cooperative control strategy is proposed. Through the collaborative control of the inverter, rectifier, and SCCs, the proposed method achieves dual-side optimal zero-voltage-switching (ZVS), wide power regulation, and maximum efficiency tracking simultaneously. Compared with the existing triple-phase-shift (TPS) method, the proposed approach improves the system efficiency across a wide range of dc output voltages and power levels. Experimental results demonstrate that the proposed method achieves a maximum efficiency improvement of up to 1.8% in the boost mode and 1.9% in the buck mode.

This article presents an extended hybrid modulation (EHM) technique to achieve multistage constant-current (MSCC) charging of electric vehicles using wireless power transfer (WPT) technology. Although most research focuses on constant-current constant-voltage charging, MSCC charging offers key advantages, such as lower temperature rise, decreased charging time, and prolonged battery lifespan. However, the existing phase-shift-modulation (PSM) method encounters substantial circulating reactive power and significant efficiency drops in MSCC charging. To overcome this, an EHM strategy is proposed to expand the modulation range of PSM. By applying EHM to both the inverter and active rectifier, the proposed method provides up to 16 operating modes to facilitate multiple CC outputs. Furthermore, an optimal mode trajectory, specifically designed for the MSCC charging, is developed. By implementing this trajectory across different charging stages, zero-voltage-switching is achieved for all power switches, and the overall power loss of the system is minimized. Finally, a WPT prototype was developed to validate the proposed approach. Experimental results demonstrate that the proposed approach effectively enables the MSCC charging while notably enhancing transmission efficiency, achieving dc-to-dc efficiencies between 92.45% and 95.67% across a power range of 231 to 3.015 kW.

High-power flexible dc links employ modular multilevel converters (MMC) for compact active power redirection in medium and high voltage grids. During contingencies, such converters may need to provide an enhanced active power capacity to avoid overload in vulnerable grid locations. This paper achieves this target by using the capacitor voltage ripple margin of the MMC submodules (SM) to enhance the dc voltage beyond the rated value. This voltage enhancement enables the enhanced active power capacity of the MMC while maintaining rated electro-thermal stresses on the components. Moreover, dynamically varying the dc side voltage reduces the MMC's circulating current, improving its operating efficiency. Because the average capacitor voltage is controlled to remain constant, the overall stresses and harmonic performance of the enhanced MMC remain the same as in the base case. In this paper, the analytical expressions for the voltage and power enhancement limits are derived, revealing a dependence on the grid-injected reactive power. Furthermore, a controller is designed to achieve stable operation during transient conditions when the power enhancement is carried out. Finally, the enhancement concept is validated using simulations and experiments with a down-scaled laboratory MMC prototype.

This study investigates the degradation behavior and reliability of silicon carbide (SiC) MOSFETs under power cycling tests to address their vulnerability to thermo-mechanical stresses. Five 650V SiC MOSFETs (IMW65R107M1H) were subjected to controlled thermal cycles, and key parameters such as body diode voltage, thermal resistance, and junction temperature were monitored. The degradation mechanisms, including bond wire fatigue and gate oxide defects, were identified through abrupt and gradual changes in the body diode voltage. A Weibull distribution was used to model the component lifetime, estimating a B-10 lifetime of 7279 cycles for devices with varying ∆Tj between 120 °C and 140 °C. Furthermore, the body diode voltage and gate leakage current were highlighted as effective precursors for early failure detection. This research provides insights into improving SiC MOSFET reliability and lays the groundwork for early warning systems in high-power converter applications.

Modular multilevel converters (MMCs) are widely used in various applications due to their scalability, efficiency, and fault-tolerant capabilities. This article proposes a fault-tolerant methodology tailored for full-bridge (FB) submodules (SMs) in MMCs to enhance system reliability under open-circuit faults (OCFs) in insulated-gate bipolar transistors (IGBTs). The method adopts a hybrid approach, using control logic adjustments to reconfigure faulty SMs into half-bridge (HB) configuration for T2/T3 faults while employing redundant SMs for T1/T4 faults. Accurate fault detection and localization are achieved through established methods, such as state observers and voltage comparisons. It is shown using MCS that the proposed method can improve the 17 kV 10 MVA converter reliability by almost 25% over solely redundancy-based solution for given lifetime requirements. Finally, using a lab-scale FB MMC prototype, it is experimentally shown that the proposed reconfiguration technique can successfully localize the fault and revert to normal operating requirements by shifting from FB to HB SM configuration in approximately 20 ms of fault initiation.

Electric aircraft represent a promising lowemission alternative to conventional fuel-powered aviation. This study proposes a battery sizing method for small all-electric aircraft using an electro-thermal battery cell model, considering different flight segments based on a reference commercial aircraft. The experiment is conducted to verify the proposed electro-thermal battery cell model. It considers varying battery efficiency throughout the flight mission, highlighting the importance of battery efficieny in the design process.

Electric aircraft represent a promising low-emission alternative to fuel-powered aviation. As the energy source, the battery pack must guarantee key performance metrics such as energy density, power density, lifetime, and safety. Among these, energy density is particularly critical as it directly impacts the range and payload capacity. Additionally, the battery thermal management system (BTMS) of the battery pack is essential to maintain safety, efficiency, and lifetime. Hence, to design a battery pack with improved energy density and optimized thermal and aging performance, a complete electro-thermal-aging (ETA) model at both cell and pack levels is developed to predict pack behavior under operational conditions. Simulations demonstrate that the proposed model achieves an accurate thermal prediction accuracy within 0.87°C during an example all-electric aircraft (AEA) mission profile. Optimization based on the proposed model is conducted, focusing on geometric configurations of the battery pack and coolant flow parameters, including channel wall thickness (L_Al), inlet width (W_cl), cell spacing (D_cell), package wall thickness (L_enc), inlet flow temperature (T_cl,in), and flow velocity (U_cl,in). An optimized liquid-cooled battery module using Samsung 18650-35E cells is designed with [L_Al, W_cl, D_cell, L_enc] = [0.4, 1.6, 20, 0.5]mm, T_cl,in =35°C, and U_cl,in = 0.05ms^-1 during cruise and 0.02ms^-1 during takeoff, climb, and descent. This configuration achieves a maximum temperature of 41.76°C and a maximum cell-to-cell temperature difference of 3.11°C, improving thermal uniformity. The lifetime performance also demonstrates a 5.51% improvement in state-of-health (SOH) after 180 cycles. Based on the module-to-pack structure analysis, the battery pack exhibits energy densities of 227.01Wh kg^-1 gravimetrically and 353.67Wh L^-1 volumetrically. This study facilitates the guideline for compact and lightweight liquid-cooled battery pack design with improved thermal and aging performance for AEA applications.

This study proposes a model predictive control (MPC) strategy integrated with closed-loop space vector modulation (CL-SVM) for a three-phase, three-level neutral point clamped (3L-NPC) rectifier supplying two alkaline electrolyzers connected in series. Electrolyzers present a nonlinear and dynamically varying load due to their dependence on temperature, pressure, and electrochemical reaction rates, imposing strict requirements on the stability and responsiveness of the power supply. Among, multi-level converter topologies, the 3L-NPC rectifier is a promising candidate for low to medium-voltage, high power applications due to its reduced harmonic distortion, improved high power handling, and balanced trade-off between complexity and performance. However, maintaining DC-link capacitor voltage balance under dynamic loads remains challenging, risking power quality and system reliability. The proposed approach optimizes voltage vector selection to regulate DC output and minimize neutral-point voltage deviation. Simulation results in MATLAB/Simulink confirm the effectiveness of the designed controller in achieving stable DC voltage and a balanced neutral-point voltage, thereby enhancing the overall performance of the power-electronics interface in electrolyzer applications.

The growth of suburbs is a challenge for public transport, and new tools are needed to electrify suburban and intercity bus lines sustainably. In-motion-charging (IMC) buses combine the advantages of both trolleybuses and electric buses. This paper analyses a case of using IMC buses on a 17-km-long intercity route service between Arnhem and Wageningen in the Netherlands. The analysis covers different traction battery technologies, sizes, and charging strategies to find the economically optimal solution. The study was carried out using a numerical model of an IMC bus, which was validated and tuned based on year-round experimental recordings obtained from Arnhem trolleybuses. The model outputs were next used to analyse the batteries ageing under specific charging-discharging current profiles. The analysis shows that the most long-term cost-effective solution for the considered case consists of using merged IMC and opportunity charging as well as a 90 kWh LTO battery, whose expected lifetime would be more than 14 years.

This paper presents the design and control of 12 kW medium voltage Modular Multilevel Converter (MMC) prototype, providing a general overview on both the component and system levels. A top level functional overview of the sub-module (SM) converter design including features like semiconductor temperature monitoring and protections for over-voltage, overcurrent, and over-temperature to enhance reliability. The paper also offers a comprehensive system overview, utilizing OPALRT as a high-level controller. To demonstrate the effectiveness of the proposed design, a 12-kW, three-phase MMC prototype was constructed, consisting of four full-bridge (FB) SMs in each converter arm. The paper explains communication management and the integration of analog and digital signals between the physical system and the user interface controller. Finally, the system's output under various operating conditions is analyzed and presented.

The energy transition encourages using heat pumps at the residential level, which results in a multi-carrier energy system when combined with PV and battery storage. Optimally controlling such systems has proven challenging. The numerous constraints required, different response times per energy carrier, and the need for forecasting methods also increase the complexity and computational cost. We propose an adaptable energy management system strategy for any system architecture with a reduced number of constraints using genetic algorithms with a discrete-continuous approach for the power setpoints. Using random forest regression, we also created short-term estimation models for the PV generation and electric and thermal demand, with error distributions centred near 0 %. Our results demonstrate that the strategy can solve the power allocation problem in the order of 1 s, including forecasting 60 minutes, minimizing electric costs, and ensuring thermal comfort.

The study presents Electric Vehicle (EV) Point-of-View (POV) Vehicle to Grid (V2G) optimisation, where the system knows the trips and energy demand of the EV. This allows it to be less conservative in the departure State of Charge (SoC) of the EV battery. The peak and cost reduction of Vehicle POV optimisation are compared with state-of-the-art Location Point-of-View optimisation, this is, the car can do V2G but it has to depart each location with at least 80% SoC as the next trip energy demand is unknown. A Mix Integer Linear Programming (MILP) V2G optimisation approach is presented to reduce peak demand and total system cost of a fleet of EVs travelling between several residential locations and one common workplace. Simulations were conducted with 26 EVs unevenly distributed between five residential locations and commuters to a common workplace. The results indicate little differences in the reduction of peak demand between both POVs of V2G optimisation. However, the system cost can be further reduced with Vehicle Point-of-View optimization, saving an additional 200 euros annually per household by transporting cheaper energy from workplaces to residential locations.

Distributed secondary control achieves voltage restoration and power sharing through communication among adjacent units but exposes the microgrid to potential cyber-attacks. Traditional mitigation strategies modify the secondary controller after the attack, addressing the issue only postoccurrence. Furthermore, in microgrid planning, the structure of the communication network significantly influences the resilience to attacks, but it remains to be explored. This article presents a proactive defense mechanism by designing a resilient communication network. The proposed method quantifies the impact of attacks and develops a multiobjective optimization algorithm to design the network, considering quantified attacks, convergence, time-delay robustness, and communication costs. The method is validated through OPAL-RT simulations of an islanded microgrid with ten converters.

The compensation and high-efficiency operation of the multimodular inductive power transfer (IPT) systems has been a challenge because of the inter- and cross-coupling between modular charging pads. This article analyzes the series–series (S–S) compensation and the associated bifurcation problem in multimodular IPT systems based on closed-form analytical modeling of the coupled circuits. From the analytical results, an improved compensation tuning method for multimodular systems is demonstrated. This improved compensation addresses the intercoupling between coils on the same side, in addition to the self-inductance of the charging pad. As a result, the system’s efficiency improves while also saving an extra capacitor compared with other circuit-based decoupling methods. In addition, a design guideline based on the sum of the coupling coefficients including cross-coupling is derived to avoid bifurcation. The phase angle of the input impedance is studied under various scenarios, demonstrating the validity of the proposed design guideline. Experimental results on a downscaled prototype show that the improved compensation method enhances efficiency by more than 2% compared with scenarios where intercoupling is not compensated, and verification of the proposed bifurcation mitigation guideline.

This article implements a real-time digital twin (RTDT) of a 10 kW Dual Active Bridge (DAB)-electrolyzer system. The electrical model of a 10 kW alkaline electrolyzer is presented to understand its I–V characteristics. A sensitivity analysis is performed to assess the impact of various parameters on the electrolyzer’s electrical characteristics. The series inductance, crucial for power transfer within a DAB converter, is examined using PLECS software to study the impact of the electrolyzer load on the peak and RMS currents. Based on this, the value of series inductance is optimized, resulting in a minimum overall RMS current throughout the operating power range. RTDT of the DAB electrolyzer system is developed using an OP4610XG real-time simulator to validate the presented model and simulation parameters. A comparison with the PLECS simulation results shows that the developed RTDT accurately operates within the 10 kW alkaline electrolyzer’s electrical characteristics. Thus, this setup exhibits the potential to evaluate power electronics converter designs without a physical electrolyzer system.

A fast and efficient charging infrastructure has become indispensable in the evolving energy landscape and thriving electric vehicle (EV) market. Irrespective of the charging stations’ internal alternating current (AC) or direct current (DC) bus configurations, the main concern is the exponential growth in charging demands, resulting in network congestion issues. In the context of exponential EV growth and the provision of charging facilities from low-voltage distribution networks, the distribution network may require frequent upgrades to meet the rising charging demands. To avoid network congestion problems and minimize operational expenses (OE) by integrating energy storage systems (ESS) into ultra-fast charging stations (UFCS). This paper presents a techno-economic analysis of a UFCS equipped with a battery ESS (BESS). To reduce reliance on the electric grid and minimize OE, a dual-objective optimization problem is formulated and solved via grid search and dual-simplex algorithms. Analytical energy and physical BESS models are employed to evaluate the optimization matrices. The intricacies of BESS aging are examined to ensure an optimal BESS size with a more extensive lifespan than the corresponding payback period. The integrated BESS significantly reduced reliance on the grid to tackle network congestion while fulfilling charging demands. The dynamic pricing (DP) structure has proven more favorable, as the average per unit cost remains lower than the static tariff (ST). Results illustrate that integrating BESS reduces the OE and peak-to-average ratio (PAR) by 5-to-49% and 16-to-73%, respectively. Moreover, the combination of 70% BESS and 30% grid capacities outperforms the other configurations with a 73% reduction in PAR and a 49% reduction in OE before BESS reaches the end-of-life.

This article proposes a transformerless, capacitively isolated converter based on a full-bridge topology with phase shift control. Compared to the existing capacitively isolated converters, the proposed design offers the advantage of zero mean voltage across the isolating capacitors, thereby reducing voltage stress. A comprehensive analytical model is developed to describe the converter’s operation in both continuous and discontinuous conduction modes. The model is validated through an experimental prototype tested at power levels up to 6 kW and switching frequencies up to 500 kHz, achieving a peak conversion efficiency exceeding 98%. The experimental results confirm the accuracy of the theoretical model and waveforms.