This paper discusses the analysis and design of a multi-port DC-DC converter using Gallium Nitride transistors for a 350V bipolar DC grid application, which could be used as the first stage to interconnect a 350V bipolar DC grid and two electric vehicle batteries. The multi-port DC-DC converter is designed with a three-level neutral-point-clamped triple-activebridge topology. The converter's parameters are selected on the basis of its performance characteristic and system specifications. Moreover, a simulation model is built to analyze the design. In the end, a prototype converter is built and the preliminary experimental results of it are shown and discussed.

This paper presents a simulation-based case study of a hybrid energy hub located at The Green Village (TGV), a living lab for sustainable innovations in Delft, The Netherlands. The energy hub integrates photovoltaic (PV) generation, battery storage, hydrogen production, seasonal storage, and usage to provide a fully electrified one-person residence, serving as a realistic testbed for the integration of renewable energy. A model of the hub is developed in Simulink/Matlab using historical operational data to simulate system behaviour under various edge case scenarios and system configurations. The model enables the evaluation of system-level interactions, operational strategies, and the impact of design choices on energy efficiency, self-sufficiency, and hydrogen integration. The simulation results show the sensitivity of the system performance to component sizing and EMS settings. This study provides valuable insights into the control and optimisation of The Green Village’s energy hub and its integrated energy systems, contributing to the practical deployment of resilient and sustainable energy hubs in the built environment.

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.

This paper presents a reinforcement learning controller (RLC) for a single-phase full-bridge rectifier as an interface for a battery energy storage system (BESS). A novel solution is presented that combines the traditional proportional-integral (PI) regulator with an RL-based control strategy using a proximal policy optimization (PPO) agent. In a high-fidelity Simulink-based digital twin setup, the agent learns to perform optimal switching actions for a single-phase full-bridge rectifier to achieve accurate current tracking and improved power quality. Simulation results show stable DC voltage regulation at 200V, tracking response under 0.1s, and harmonic compliance with THD equal to 2.38%. The hybrid control strategy guarantees robust dynamic performance and adaptability in the context of renewable energy and storage systems’ varying source and load conditions. The findings demonstrate the potential of coupling AI-driven control with digital twins to empower the autonomy and resilience of future smart energy systems.

In recent years, several common-ground switched-capacitor transformerless (CGSC-TL) dc–ac multilevel power converters have been introduced, providing advantages such as multilevel output voltage, voltage boosting, and mitigated leakage current. However, these structures mostly suffer from drawbacks, such as limited output voltage levels (like only five levels), lack of voltage-boosting capability, and high charging current spikes of the capacitors. This article proposes a new single-stage CGSC-TL nine-level (9L) multilevel inverter (MLI) with voltage-boosting capability and limited spikes of charging current of the capacitor, designed to be employed as a single-stage power-electronics-based interface device between renewable energy sources, such as photovoltaic (PV) systems and power grid and/or load. The proposed MLI provides several merits, such as a common-ground structure that suppresses PV-to-ground leakage current associated with PV parasitic capacitances, active and reactive power support, a wide input voltage range, and higher output voltage levels (9L) compared with other structures in the same class. Comprehensive comparative analyses, as well as simulation and experimental results, are presented to verify the performance of the proposed inverter.

This article presents a new transformerless switched-capacitor (SC) based five-level grid-connected inverter with inherent voltage-boosting capability. The proposed topology achieves a voltage gain factor of two without requiring an additional dc-dc boost converter or transformer, resulting in a more compact, cost-effective, and efficient design. A single SC cell is utilized to perform bidirectional capacitor charging during both positive and negative grid half cycles, thereby improving energy transfer efficiency and significantly reducing capacitor size and volume compared with the conventional topologies. The inverter employs a minimal number of components - only nine switches and one flying capacitor - while maintaining high performance. Only five switches operate at high frequency, which reduces switching losses, gate driver complexity, and electromagnetic interference. A straightforward control strategy ensures that the inverter delivers a high-quality sinusoidal current waveform to the grid and supports both active and reactive-power flow under various power factor conditions. The reliability of the proposed inverter is analyzed, and its performance is validated through detailed simulations and experimental results. A comparative study with the existing solutions highlights the advantages of the proposed topology in terms of efficiency, voltage gain, component count, and waveform quality.

The power versus voltage curve of a photovoltaic (PV) panel exhibits several maximum power points (MPPs) in a partial shading (PS) condition. Thus, it remains an optimization challenge to ensure that PV systems operate at their global MPP (GMPP). Scanning the output characteristics of the PV panels seems a general solution for this issue. However, applying a short circuit to the terminal of PV panels where there exists an electrolytic capacitor, has a detrimental effect on the lifetime of the system. To this end, in this article, a GMPP estimator is proposed as a global solution for conventional maximum power point tracking (MPPT) algorithms under PS conditions. The proposed technique improves existing simple MPPT algorithms with original approaches as follows: first, an accurate microscopic analysis of a PV characteristic in PS conditions is considered, second, an original definition of the dominant cells and modules in a PV panel is proposed that allows to reduce the PS patterns to a finite number, and third, the search area for the MPPT operation is reduced to find the accurate GMPP by proposing two voltage boundaries. The lower boundary corresponds to the GMPP under uniform shading condition that can be determined using a closed form formula, while the upper one refers to the GMPP of a dominant cell in a PV module that can be determined using an artificial intelligence technique. This can also help set the initial duty cycle in a convex area around the GMPP. The functionality of the proposed GMPP estimator is experimentally validated.

This article presents a fully soft switched, non-isolated high voltage gain single magnetic core multiport converter based on boost three port structure for renewable energy applications. The developed voltage multiplier cell integrates switched capacitor and coupled inductors techniques to achieve high voltage gain and more design freedom. Furthermore, only a single magnetic core is employed which contributes to higher power density while an active clamp cell is integrated to provide fully soft switching condition and eliminate capacitive turn-on loss. All switches operate at zero voltage switching and diodes turn off at zero current switching. This approach minimizes switching losses and clamps the voltage spikes which enables the use of low forward voltage diodes. Also, the switches voltage stress is reduced through utilizing coupled inductors technique and thus, switches with low drain-source resistance can be utilized to achieve high efficiency along with high power density. To validate the theoretical analysis, a 200 W prototype with 400 V output port is implemented and the experimental results are presented.

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 the 5-level Packed U-Cell (PUC5) converter as an active front-end (AFE) rectifier designed to achieve high power quality and power factor correction (PFC) in grid-connected applications. Operating in boost mode, the proposed topology significantly mitigates input current harmonics and enables precise regulation of the DC output voltage, making it well-suited for bidirectional DC systems such as electric vehicle (EV) charging infrastructure with Vehicle-to-Grid (V2G) capability. A detailed analysis of the converter’s switching states is conducted, with a focus on identifying redundant states that can be strategically utilized to balance the voltage of the auxiliary capacitor. This balancing is essential to sustain the five-level voltage waveform at the rectifier input, which in turn minimizes voltage distortion and directly reduces current harmonic content. The output voltage is tightly regulated to supply downstream DC loads with high stability. Experimental validation confirms the effectiveness of the proposed PUC5 rectifier, demonstrating its ability to operate with a near-unity power factor while drawing low-distortion current from the AC grid and maintaining robust dynamic performance under varying load conditions.

Electric vehicles (EVs) offer a promising solution for mitigating greenhouse gas emissions and minimizing the transportation sector's dependency on non-renewable energy sources. However, efficient energy management poses a significant challenge for their broader adoption, particularly optimizing battery usage, maximizing driving range, and improving overall vehicle performance. This paper presents the state-of-the-art Artificial Intelligence (AI) techniques used in electric vehicle energy management systems (EV-EMS), discussing a variety of deep learning algorithms of AI methodologies, such as, neural networks, and fuzzy logic. Additionally, This paper discusses the role of auxiliary techniques like transfer learning, which enhances model adaptability and reduces training time in AI-driven EMS applications. Through a systematic analysis of each method, this review identifies key trends, highlights the challenges and limitations of each technique, and offers perspectives on potential solutions and future research directions. The paper aims to support researchers, industry professionals, and policymakers in developing advanced, sustainable, and adaptable EV-EMS solutions that maximize battery life, improve vehicle performance, and facilitate real-time adaptive control. Finally, this review highlights the importance of AI-driven strategies in making EV technology more efficient, reliable, and scalable.

Photovoltaic (PV) systems are frequently subject to voltage and current mismatches caused by various factors such as partial shading, differing panel tilt angles, dust accumulation, and cell degradation among PV elements. These mismatches can significantly reduce the overall efficiency of PV systems by preventing individual modules or strings from operating at their maximum power point (MPP). This article introduces a novel architecture termed PV to Virtual Bus Series-Parallel Differential Power Processing, which effectively mitigates mismatches in both series-connected PV modules (i.e., current mismatches) and parallel connected PV strings (i.e., voltage mismatches). The proposed architecture employs a combination of string level converters (SLCs) and module-integrated converters (MICs) that process only a fraction of the total power. Notably, the architecture leverages virtual buses on the primary side of both SLCs and MICs, leading to reduced voltage rating requirements for SLCs and lower power rating demands for MICs. This design reduces the stress on individual components, making the system more cost-effective and reliable. The article provides a comprehensive analysis of the requirements for SLCs and MICs, along with a detailed explanation of how the proposed architecture ensures that PV modules consistently operate at their respective MPPs. Additionally, it explains how the virtual bus voltage is balanced through mathematical power flow equations, ensuring stable and efficient operation. Finally, the architecture's effectiveness is validated through real time simulation results with two PLECS RT Box, which demonstrate its capability to address mismatch issues and optimize the performance of PV systems.

This article introduces a new fully soft switched ultra-high step-up quadratic converter. The proposed converter benefits from several advanced features including high voltage gain at low duty cycle, low switch voltage stress, low sum of diodes voltage stress, switching at ZVS condition for switches, low ripple continuous input current, and common ground between the input and load. These advantages are achieved by integrating the quadratic structure with an auxiliary circuit consisting of coupled inductors, switched capacitors, and active clamp techniques. Furthermore, the presented converter mitigates the reverse recovery problem of all diodes especially the input side diodes which is a challenge in many quadratic base converters. These properties have contributed to providing an efficient converter with wide applicability. The converter is fully analyzed, its superiority to other structures is shown, and a 200 W laboratory prototype validates the theoretical analysis.

This paper introduces a novel single-loop control scheme for voltage regulation in islanded inverters, using a proportional-integral-lead (PI-Lead) controller designed within the synchronous reference frame (SRF) through a loop-shaping approach. Commonly, dual-loop controllers have been employed for this purpose owing to several limitations, such as insufficient stability with a narrow gain margin, a trade-off between stability and bandwidth, and constrained bandwidth due to the need for a significantly lower outer voltage loop bandwidth compared to the inner current one. The proposed method overcomes these challenges by integrating a Lead compensator, which enhances voltage regulation by eliminating steady-state error, improving stability margins, and providing a fast transient response while maintaining robustness against model parameter variations. Additionally, the control strategy reduces dependence on current measurements, except when dealing with inductive loads where virtual resistor-based active damping is necessary. Despite the challenges posed by multi-resonance phenomena and coupling effects inherent in single-loop SRF-based modeling, a comprehensive frequency-domain analysis is performed, with systematic controller parameter design guidelines to mitigate multi-gain crossover issues. Rigorous experimental results validate the theoretical findings and simulations, demonstrating the superior performance and practical effectiveness of the proposed control strategy compared to existing methods.

This article proposes the use of a proportional–integral–derivative (PID) controller as an adaptive mechanism within the framework of the model reference adaptive system-based rotor flux (MRASF) for accurate rotor speed estimation in induction motors. The controller is designed to impose specific dynamic behaviors and performance criteria, addressing the sensitivity of MRASF dynamics to slip speed. To achieve this, a full-order transfer function of the MRASF is employed to systematically derive the PID parameters using compensation and pole placement techniques. The proposed control strategy ensures that the estimated rotor speed closely follows the desired performance across various operating conditions. The effectiveness of the MRASF–PID design is validated through both simulation and experimental testing under open-loop voltage/frequency control of the induction machine, confirming the successful realization of the targeted dynamic and steady-state performance.

Renewable Energy Sources (RESs), particularly Photovoltaic (PV) systems, inherently produce variable DC voltages that often cannot meet load or grid requirements directly. Consequently, a reliable and efficient DC-DC converter is required to interface RESs with downstream converters and loads. This paper proposes a non-isolated, high-gain, transformerless step-up DC-DC converter that provides continuous input current and a non-inverting output voltage. The proposed topology is designed to minimize switching losses and to maintain a low component count, thereby improving conversion efficiency while containing cost and implementation complexity. A comprehensive analytical model that includes parasitic elements is developed to derive the converter voltage gain. Comparative analyses of device voltage and current stresses against recently reported topologies are presented. The results demonstrate that the proposed converter achieves high voltage gain with reduced voltage and current stresses on switching devices, while preserving acceptable component current levels. Conduction intervals of switches and diodes, as well as switching loss contributions, are analyzed and quantified. Experimental validation is provided by a 100 W prototype, and measured results corroborate the theoretical predictions and simulation outcomes. The proposed converter thus represents an effective and practical solution for high-gain DC–DC conversion in renewable energy applications, offering an advantageous trade-off between efficiency, component simplicity, and cost.

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.

This paper introduces a soft-switched high step-up converter applicable to photovoltaic systems. In the proposed topology, to further improve the voltage gain and reduce the components voltage stress, coupled inductors and voltage multiplier cell (VMC) techniques are integrated with the conventional boost converter. Hence, it addresses challenges in traditional boost converter when operating at near unity duty cycle and enables it to utilize high-quality components that lead to decreased conduction losses in high-output voltage applications. Furthermore, the converter achieves soft switching operation by incorporating a lossless snubber cell, which consists of just two diodes and requires no additional switches and magnetic cores. These features contribute to enhancing the converter efficiency. Notably, the energy stored in the snubber circuit is effectively recovered to the output without any circulating current, making it a beneficial characteristic among the lossless snubber structures. The proposed topology also offers a common ground between the input and output terminals, as well as the switch which simplifies the converter control circuit. Additionally, it maintains continuous input current, which makes the proposed converter more suitable for photovoltaic system applications. To validate the converter benefits, a 150 W laboratory prototype converter is implemented.

Owing to the intermittent characteristics of renewable energy sources (RESs) and the unpredictability of load demand, integrating multiple RESs and energy storage systems (ESSs) has become imperative. Modular Multiport Converters (MMPC) have emerged as a viable solution to meet this need, offering superior performance, efficiency, and reliability compared to multiple SISO DC/DC converters. To this end, this paper presents a comprehensive model of an MMPC, which is bidirectional and capable of operating in both step-up and step-down modes. Following the derivation of the converter model, a robust μ-controller using the D − G − K iterative procedure is designed. This controller addresses the cross-coupling challenges inherent in MIMO systems and effectively overcomes the parametric uncertainties associated with the converter. Finally, hardware-in-the-loop (HIL) test results derived from OPAL-RT 4610, and experimental results from a prototype are used to validate this control approach.

With the expanding share of wind energy in power grids, accurate forecasting has become critical for maintaining system stability and operational efficiency. Notwithstanding, forecasting accuracy is compromised by uncertainties from fluctuating wind speeds and meteorological conditions. This paper proposes a novel multi-phase short-term wind power forecasting framework (multi-step ahead forecasting over a 1-hour horizon). Thus, decomposition of the wind power signal and feature extraction are initially implemented using Variational Mode Decomposition (VMD) and Principal Components Analysis (PCA), respectively, aiming to enhance input quality and reduce computational burden. The proposed forecasting model is built on a hybrid DL architecture merging a Convolutional Neural Network (CNN), Attention Mechanism (AM), and Deep Feedforward Neural Network (DFFNN). Given the impact of decomposition levels and extracted PCA components count on forecasting performance, a search-based scheme is developed to explore a pre-defined space (maximum decomposition level and extracted components count) to determine the optimal configuration for each interval. In the next phase, a Fuzzy Decision-Making (FDM) technique is employed to select a balanced and optimal configuration for the proposed model across the year. To demonstrate the proposed architecture’s efficacy and generalizability, the model is tested on two real-world data from La Haute Borne wind farm in France and Hill of Towie wind farm in Scotland. Results demonstrate that the proposed architecture with the selected configurations achieves significant accuracy and generalization, with average NRMSEs and NMAEs values of 0.428% and 0.333% for La Haute Borne wind farm and 0.502% and 0.381% for Hill of Towie wind farm.