High-gain isolated DC–DC converters are essential for interfacing low-voltage renewable sources such as photovoltaic (PV) modules and fuel cells with the high-voltage DC links required in electric vehicle (EV) traction and charging systems; however, existing high step-up designs typically rely on multi-switch or magnetically complex topologies and are validated only under short-term laboratory conditions, leaving critical gaps in long-term stability, spectral behaviour, and real-field performance. This work presents a low-cost isolated high-gain DC–DC converter capable of boosting 8.5–12 V inputs to as high as 700 V using a single high-frequency transformer and a simple SG3525–LM393 analogue voltage-mode control architecture, thereby reducing component count and implementation complexity while maintaining high step-up capability. Comprehensive laboratory characterization – including steady-state gain measurement, transformer flux balance verification, transient line/load disturbance evaluation, switching waveform inspection, and Fast Fourier Transform-based electromagnetic analysis – confirms continuous-conduction-mode operation with stable high voltage gain, minimal ripple, strong closed-loop regulation, and effective switching-noise suppression with harmonic amplitudes below 20 dB up to 5 MHz. Outdoor experiments conducted using a commercial solar PV panel further demonstrate the converter's robustness under naturally varying irradiance, reliably producing up to 700 V without auxiliary conditioning. To evaluate long-term behaviour, 990 operating samples collected over 90 days are analysed using a CatBoost regression model, achieving R 2 = 0.9705, MAE = 8.08 V, and RMSE = 11.22 V, thereby validating the suitability of machine-learning techniques for performance prediction and digital twin development in high-gain converters. The combined results establish that the proposed topology offers a unique combination of high voltage gain, excellent electromagnetic compatibility, low implementation cost, simplified analogue control, and experimentally verified long-term field reliability, making it a strong candidate for renewable-energy-powered EV systems, high-voltage DC-link applications, and distributed DC microgrid environments.
This study investigates a solar-driven multigeneration system integrating a parabolic trough collector, an organic Rankine cycle (ORC), and a thermal desalination unit to produce electricity, freshwater, and useful heat. The main contribution is the utilization of a ternary working-fluid mixture (R152a/R1234yf/R600a) and the systematic multiobjective optimization of its mass fractions. A comprehensive thermodynamic model was developed, validated, and evaluated for two representative operating days, 21 December and 21 June. Weighted-sum multiobjective optimization was performed across three scenarios. The power-priority and water-priority scenarios yielded optimal mass fractions of 0.45/0.34/0.21 and 0.41/0.26/0.33, respectively. The balanced scenario, weighting net power output, condenser heat duty, and freshwater production rate equally, identified a recommended composition of 0.38/0.22/0.40 with an aggregate index of 0.7076. Under this optimal mixture, the system delivered a net power output, condenser heat duty, and freshwater production rate of 1060 W, 13,870 W, and 20.45 kg h −1 on 21 December, and 1475 W, 19,750 W, and 28.80 kg h −1 on 21 June, respectively. The recommended mixture also showed a low mass-weighted global warming potential of 54 and lower cost compared to pure R1234yf. Furthermore, component-level exergy analysis identified the solar collector field as the dominant source of irreversibility, accounting for 68.5% and 73.2% of the total exergy destruction on 21 December and 21 June, respectively, followed by the ORC boiler with 12.8% and 10.5% shares. These results demonstrate that while composition optimization balances system yields, future thermodynamic improvements should focus on solar collection and heat addition.
To improve the trajectory control accuracy of directional drilling for advanced geological investigation, this study systematically investigates the composition, propagation law, and uncertainty characterization method of measurement errors based on theoretical calculations and engineering verification. The results indicate that the inclination measurement error primarily originates from inherent instrument errors, borehole curvature effects, and cuttings bed lifting effects. Azimuth error is significantly disturbed by geomagnetic anomalies, while hole depth error is mainly attributed to drill pipe machining tolerances and thread connection clearances. A two-plane ellipse characterization model is established based on error propagation theory. The four semiaxis lengths of the ellipse are determined through eigenvalue calculation of the borehole trajectory covariance matrix, which exhibits a nonlinear sensitivity to angle variations. Comparative analysis demonstrates that the deviation induced by hole depth measurement errors is smaller than that caused by angular errors, with the proportional contribution of these errors varying across different drilling positions. Simulation analyses of kilometer-scale straight boreholes reveal that measurement spacing exerts a dominant control on error accumulation. As the measurement spacing increases from 3 m to 18 m, the elliptical semiaxis length increases logarithmically, with a maximum growth rate of 160%. This finding verifies that dense measurement points can effectively suppress error accumulation. Field engineering tests further validate the accuracy of the proposed characterization model. Specifically, the model achieves a prediction accuracy of 50% at a confidence factor of 1 and 100% at a confidence factor of 2.
This article presents a comprehensive and critical review of power quality issues (PQIs) arising from the integration of electric vehicles (EVs) into modern power grids, particularly under high penetration scenarios. As EV adoption accelerates globally—driven by decarbonization goals, government policies, and advances in battery and charging technologies—its impact on grid infrastructure has become a significant concern. The study systematically examines the power disturbances introduced by EV charging systems, differentiating between unidirectional grid-to-vehicle and bidirectional vehicle-to-grid (V2G) operations. Key PQIs such as harmonic distortion, voltage sags and swells, reactive power imbalance, frequency deviations and voltage unbalance are investigated for different charging levels (Level 1, Level 2, direct current fast charging), charger topologies and battery states of charge. We pay special attention to the compounding effects of high EV penetration, where stochastic and simultaneous charging behaviors worsen grid instability, transformer overloading, and communication interference. The review discusses conventional mitigation approaches such as passive and active harmonic filters, distribution static compensators, grid-supportive inverter topologies and other emerging solutions including smart charging algorithms, machine-learning-based predictive control, adaptive reactive power compensation and integration of renewable energy sources. The importance of bidirectional charging in providing ancillary services such as frequency control and peak shaving is highlighted, and new PQIs originating from dynamic switching of modes, supraharmonic generation and failures in communications in V2G are identified. A gap analysis identifies the need for dynamic grid models, secure communications protocols (ISO 15118, IEEE 2030.5) and technoeconomic assessment of the grid for large-scale implementation of EVs. The novelty of this review is that it adopts an integrated approach by considering all three aspects to evaluate the issues and opportunities associated with EV integration from a system-wide perspective. The present study offers important insights by exploring trends in current research, identifying gaps, and suggesting possible solutions.
Accurate short-term wind power prediction plays a critical role in ensuring stable grid operation, effective energy management, and the large-scale integration of renewable energy systems under highly variable wind conditions. Although data-driven and deep learning models have demonstrated promising forecasting capability, many existing approaches suffer from performance degradation during rapid wind fluctuations due to the lack of embedded physical constraints and the high computational complexity associated with recurrent architectures. To address these limitations, this article proposes a Physics-Guided Residual Temporal Convolutional Network (PG-ResTCN) for short-term wind power forecasting. The proposed framework integrates dilated temporal convolutional learning with residual connections to effectively capture multiscale temporal dependencies in wind power time series while avoiding the sequential computation of recurrent neural networks, thereby improving computational efficiency. Furthermore, a physics-based smoothness constraint is incorporated into the training loss function to enforce physically consistent power ramping behavior that reflects the inherent operational dynamics and inertia of wind turbines, reducing unrealistic fluctuations in predicted power outputs. The effectiveness of the proposed model is validated using a large real-world dataset containing approximately 140,161 hourly samples collected from five wind farm locations, including meteorological variables and corresponding turbine power outputs. Comprehensive experiments are conducted by comparing the proposed method with widely used benchmark models, including Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory networks. Results demonstrate that the proposed PG-ResTCN model achieves superior forecasting performance, obtaining a root mean square error of 0.0359, mean absolute error of 0.0296, and R 2 of 0.9759, outperforming all baseline models. The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability. In addition, the proposed framework maintains high computational efficiency and robustness, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operations.
A mathematical model was developed and experimentally validated to predict the thermal performance and drying behavior of an indirect active solar dryer (IAHSD) for mint leaves. The distinctive contribution of the proposed approach is its integration of solar-energy input, auxiliary gas heating, controlled fresh–recirculated air mixing, ambient-humidity effects, chamber heat losses, and mint-leaf moisture removal within a computationally accessible model suitable for operational assessment and control-oriented applications. The model describes coupled heat and mass transfer processes while considering key operating parameters, including drying air temperature (50–60°C), air recirculation ratio (70–90%), and ambient relative humidity (20–80%). Simulation results showed that increasing drying air temperature and recirculation ratio enhanced the drying chamber temperature, whereas higher ambient humidity reduced the thermal level and slowed moisture removal. Predicted chamber temperatures ranged from 37.83°C to 67.31°C depending on the inlet air temperature, while experimental values followed similar trends but were slightly lower due to environmental variations. Maximum temperatures occurred near midday, highlighting the influence of solar radiation on system performance. The model also captured moisture removal dynamics, indicating that higher drying temperatures accelerated drying rates, while elevated humidity reduced evaporation efficiency. Under low temperature and high humidity conditions, temporary moisture absorption was observed due to reversed vapor pressure gradients. Model validation showed strong agreement between predicted and measured data, with coefficients of determination (R 2 ) ranging from 0.85 to 0.96, confirming the reliability of the proposed model.
In this study, the high-resolution numerical simulation of the three-dimensional radiative rotating flow of polyalphaolefins (PAOs)-based nanolubricant with synergistic dispersion of molybdenum disulfide (MoS 2 ) and silicon dioxide (SiO 2 ) hybrid nanoparticles over a permeable surface expansion is presented. Motile bioconvection microorganisms are incorporated in the hybrid nanofluid to maximize the convective thermal performance and to stabilize the matrix of the multiphase solutions. The computational fluid dynamics framework considers the multiphysical coupled effects of the Darcy–Forchheimer porous medium, thermal radiation, heat sources and convective Biot boundary conditions with an aligned magnetic angle. The numerical approach, BVP4c is used to solve the transformed nonlinear equations of the boundary layer, and response surface methodology is applied to the surface topologies for the statistical mapping of the interactive limits of the parameters. It is found from the quantitative evaluations that the dominance of the porosity parameter is that it exactly gives a 67.4012% increase in the secondary skin friction as the porosity factor increases from 0.5 to 2.5. Moreover, the effect of an aligned magnetic field improves the energy transfer rate by 77.47%. The results of a rigorous grid independence matrix test show that when the number of discrete grid nodes is increased beyond ( N = 320), all boundary layer fluxes are asymptotically stabilized to within six decimal places. Consequently, absolute convergence results show that the absolute error residuals are always kept within a stable micro-band until 10 −8 , demonstrating the unprecedented numerical accuracy, stability and reliability of the present simulation for advanced microfluidic thermal management systems.
Understanding the interactions among energy consumption, economic growth, foreign direct investment (FDI), and environmental quality is essential for achieving sustainable development in emerging economies. Although numerous studies have examined these relationships, limited evidence exists for the Philippines, particularly regarding the integrated role of electricity consumption, fossil fuel dependency, renewable energy adoption, and FDI in shaping carbon emissions. This study investigates the dynamic nexus among these variables using annual data for the period 1990–2023. The dynamic Autoregressive Distributed Lag framework is employed to estimate both long-run and short-run relationships and to simulate the adjustment paths following changes in key explanatory variables. The results reveal a long-run equilibrium relationship among the variables. Fossil fuel consumption is identified as the primary driver of carbon emissions, while renewable energy contributes to emission mitigation. Economic growth and electricity consumption increase environmental pressure, whereas FDI promotes economic development without exerting a significant direct impact on emissions. The findings suggest that transforming the energy structure rather than restricting electricity demand represents the most effective pathway toward emission reduction. This study contributes to the literature by providing an integrated assessment of the energy–economy–environment nexus and generating policy-relevant evidence for the Philippines and other developing economies undergoing energy transition.
This study proposes and evaluates a novel solar–biomass hybrid multigeneration system from thermodynamic, exergoeconomic, and environmental perspectives. The integrated configuration consists of an intercooled gas turbine cycle, a photovoltaic thermal-assisted polymer electrolyte membrane (PEM) electrolyzer for hydrogen production, and a waste-heat recovery subsystem incorporating an organic Rankine cycle and a refrigeration unit. In the proposed layout, biomass is used as the primary fuel in the main combustor, while hydrogen is employed in the secondary reheating stage to enhance operational flexibility and overall system performance. Parametric analyses show that the overall energy and exergy efficiencies initially increase with the low-pressure compressor pressure ratio, reaching maximum values of 0.233 and 0.192, respectively. The incorporation of intercooling improves the energy efficiency from 0.214 to 0.233 and the exergy efficiency from 0.174 to 0.192. In addition, increasing the effectiveness of the heat-recovery heat exchanger from 0.70 to 1.00 reduces the total fuel consumption from 0.0335 kg/s to 0.0240 kg/s and decreases the exergy destruction in the primary combustor from 152 kW to 128 kW. Hydrogen integration also improves the environmental performance of the system by reducing the CO 2 emission rate from 0.01782 kg/s to 0.0098 kg/s, corresponding to a 45% reduction. Exergoeconomic analysis indicates that the PEM electrolyzer is the most cost-intensive component, with a cost rate of 15.65 USD/h. Overall, the proposed system demonstrates strong potential for the sustainable and efficient cogeneration of power, heating, cooling, and hydrogen.
In modern power systems, the integration of distributed energy resources creates challenges for coordinated control, operational stability, and secure energy transactions, particularly under extreme climate conditions. This work presents a simulation-based proof-of-concept framework comprising a multi-agent reinforcement learning (MARL) coordinator and a permissioned blockchain layer, designed to enhance transparency, resilience, and security in microgrid operations. The hybrid Convolutional Neural Network–Long Short-Term Memory forecasting model predicts load, solar generation, and electricity prices, guiding a multi-agent Proximal Policy Optimization controller. Autonomous agents manage battery storage, generator operation, and grid power exchanges. Energy transactions are represented and validated within a simulated permissioned blockchain layer. The performance of the framework is evaluated within a validated digital-twin microgrid simulation environment, featuring a 350 kW PV array and a 300-kWh battery system, including a 48-h simulated heatwave to assess climate resilience. Compared with rule-based control and MPC, the proposed system demonstrates reductions in operational cost up to 22.8% and improvements in energy efficiency by 24.7%, while achieving high reliability under simulated conditions (99.92%). Under climate stress, the framework achieved 99.1% load satisfaction with only an 18% cost increase. The blockchain layer introduces negligible overhead (<0.5% energy use). Technoeconomic analysis indicates economic feasibility, yielding a 28.5% annual return and a 30-month payback period. Overall, the proposed framework offers a scalable simulation-based approach for investigating secure and climate-resilient smart energy management strategies under complex operating conditions.
This article presents a scalable and energy-efficient bridgeless canonical switching cell (CSC) based power factor correction (PFC) converter integrated with finite control set model predictive control (FCS–MPC) for low-voltage electric vehicle battery charging under universal grid conditions. Conventional boost-derived PFC converters suffer from increased conduction losses due to front-end diode bridge rectifiers and exhibit limited dynamic performance during grid disturbances; to address these limitations, the proposed topology eliminates the diode bridge and exploits the intrinsic energy transfer characteristics of the CSC to enhance power density, reduce semiconductor stress, and improve current shaping capability. A discrete-time predictive model is developed to simultaneously regulate the DC-link voltage and shape the grid current, and a multiobjective cost function is formulated to minimize current and voltage tracking errors while reducing control effort. The FCS–MPC directly generates optimal switching states without conventional pulse width modulation modulation, ensuring fast transient response and lower computational complexity compared to proportional-integral-based and artificial intelligence-assisted control strategies reported in recent literature. Designed for a 7.4 kW, 51 V lithium-ion electric vehicle battery system suitable for light electric vehicles such as e-rickshaws, the converter demonstrates stable operation across a wide input voltage range of 85–265 V AC, achieving a near-unity power factor (0.9999) and total harmonic distortion (THD) as low as 1.09% under voltage sag and swell conditions, consistently maintaining THD below 2% across global grid standards. Furthermore, the architecture is scalable up to 7.4 kW without modification of the control framework, enabling compatibility with both low-voltage and higher-power EV charging applications. The results confirm that the proposed CSC–MPC framework provides an effective balance between harmonic mitigation, computational efficiency, scalability, and grid compliance, offering a robust and sustainable solution for next-generation EV charging infrastructure.
Accurate prediction of photovoltaic (PV) ramp events is essential for maintaining grid stability and ensuring reliable operation of renewable-rich power systems. However, conventional evaluation metrics often fail to reflect the operational consequences of forecasting errors under asymmetric cost conditions. This study proposes a decision-aware framework for PV ramp event prediction that explicitly links predictive performance to operational decision quality. A rigorous temporal evaluation methodology combining leave-one-month-out validation with an independent fixed test set is adopted to ensure realistic generalization assessment. Three machine learning models, namely LogitBoost, Random Forest, and Support Vector Machines, are evaluated, with LogitBoost achieving the best overall predictive performance (area under the ROC curve (AUC) = 0.9468, F1-score = 0.7140, and Precision-Recall AUC = 0.7185). The results demonstrate a substantial discrepancy between the F1-optimal threshold and the operational cost-optimal threshold, indicating that conventional metric optimization may lead to suboptimal operational decisions. Post-hoc threshold optimization is further compared with cost-sensitive learning approaches under asymmetric penalties assigned to false positives (100 €) and false negatives (500 €). Although the investigated cost-sensitive learning approaches improve recall, they generate more false alarms and do not achieve the lowest operational cost under the considered evaluation setting, whereas the proposed post-hoc framework yields lower operational expenditure. Furthermore, the analysis reveals a strong dependence of decision quality on weather variability. To address this issue, regime-adaptive decision thresholds are calibrated on an internal forward-chaining validation subset of the training data and subsequently applied unchanged to the independent December test set, thereby ensuring a leakage-free evaluation. On the independent test set, the proposed adaptive strategy reduces operational cost by 3.0% (from 50,400 € to 48,900 €) while improving recall from 0.911 to 0.926 with only a marginal reduction in precision. Within the evaluated PV ramp forecasting setting, the proposed decision-level optimization framework achieves lower operational cost than the investigated cost-sensitive learning approaches, while the low-overhead regime-adaptive thresholding strategy provides additional operational improvements without modifying the predictive model.
Latent heat thermal energy storage is an effective approach for enhancing the performance of solar water heating (SWH) systems by storing surplus thermal energy and delivering it during periods of low solar availability. In this study, the thermal performance of a solar water heater integrated with a phase change material (PCM) storage unit is experimentally investigated under real outdoor operating conditions. Paraffin wax is employed as the PCM, while water serves as the heat transfer fluid (HTF). Thermal energy is transferred from the solar collector to a spiral copper coil heat exchanger embedded within the PCM tank, enabling indirect heat exchange between the HTF and the storage medium. The charging and discharging processes were examined on clear-sky days by monitoring the temperature evolution of the PCM and HTF using a distributed thermocouple array. The effects of HTF inlet temperature and flow rate on the thermal response, energy storage capacity, and system efficiency were systematically analyzed. The results demonstrate that the PCM-based storage system significantly enhances the thermal performance of the solar water heater. At a representative HTF inlet temperature of 80°C, the charging and discharging efficiencies reached 78.2% and 61.1%, respectively, while higher inlet temperatures led to further efficiency enhancement. During the charging process, the PCM stored 2.8–3.0 MJ of thermal energy, depending on the operating conditions, confirming its substantial latent heat storage capacity. During discharge, up to 1.65 MJ of useful thermal energy was recovered over 45–50 min at an inlet water temperature of 25°C and a flow rate of 48 L/h. Overall, integrating a PCM-based storage unit with a spiral coil heat exchanger improves the energy storage capacity and thermal efficiency of SWH systems, thereby extending hot water availability beyond periods of solar radiation.
Transitioning toward global carbon neutrality requires developing energy conversion and storage technologies that surpass current solutions in efficiency, durability, and scalability. In this context, nanocatalysts have emerged as indispensable for advancing clean energy systems, owing to their tunable surface chemistry, structural diversity, and potential for defect engineering. In this review, we present a reproducible roadmap that links nanoscale design strategies to practical applications and eventual industrial adoption. Beginning with fundamental design approaches including morphology manipulation, alloying, heterostructuring, and hierarchical architectures, the review outlines how these strategies enhance electrocatalytic activity, specificity, and stability. The discussion further encompasses nanocatalyst applications in water splitting for sustainable hydrogen production, fuel cells for efficient electrochemical conversion, and advanced energy storage technologies, including batteries, supercapacitors, and hybrid systems. From an industrial perspective, the review also examines scalable synthesis, electrode fabrication, techno-economic evaluation, and life-cycle assessment. Distinct from previous reviews, this work emphasizes the role of artificial intelligence and machine learning in accelerating catalyst development through high-throughput discovery, predictive modeling of catalytic performance, and digital twin-based durability assessment. By integrating nanoscale innovations with pathways for industrial translation, this review provides a comprehensive roadmap for deploying nanocatalyst technologies at scale within renewable energy infrastructure. These insights are critical for mitigating greenhouse gas emissions and fostering sustainable, cost-effective, and commercially viable clean energy technologies.
Biogas supports sustainable energy transitions in rural Africa while advancing climate action, gender equality, and ecosystem conservation. However, evidence of its multidimensional impacts remains fragmented. This systematic review synthesizes 73 studies published between 2015 and 2025 to examine how biogas adoption contributes to SDG 7 (clean energy), SDG 5 (gender equality), SDG 13 (climate action), and SDG 15 (ecosystem protection), while highlighting adoption drivers, regional variations, and research gaps. Relevant literature was identified through Boolean searches in Scopus, Web of Science, Google Scholar, and institutional sources, and screened following a PRISMA-guided approach. Data were thematically analyzed, coding outcomes by SDGs, adoption determinants, and regional contexts. Findings show that biogas improves household energy access, reduces women's labor and health burdens, lowers greenhouse gas emissions, and mitigates deforestation while supporting biodiversity. Strong synergies across SDGs indicate that biogas functions as an integrated development solution rather than a single-sector technology. Yet adoption remains uneven due to high upfront costs, limited finance, weak institutional support, and socio-cultural barriers. This review provides combined evidence of biogas's systemic role in advancing multiple SDGs in Africa and underscores the need for context-specific policies, inclusive financing, and longitudinal, multidimensional research to enable sustainable scale-up.
The growing global demand for sustainable and efficient energy systems has accelerated advancements in solar cell technologies. This review presents a systematic and quantitative analysis of recent developments in solar cells enabled by nanotechnology. A PRISMA-based methodology is adopted to ensure transparency and reproducibility in literature selection. Key nanomaterials such as perovskites, quantum dots, graphene, and carbon nanotubes are critically evaluated based on power conversion efficiency (PCE), stability, cost, and environmental impact. Additionally, nano-enhanced thermal systems including photovoltaic-thermal (PVT) systems, nanofluids, and phase change materials (PCM) are analyzed using thermodynamic principles. The review highlights major challenges such as degradation mechanisms, scalability, and toxicity while proposing a future research roadmap focused on stability engineering and sustainable material design.
Water scarcity in arid regions, such as Kabul, presents a critical challenge, further complicated by the inherent thermodynamic inefficiencies and high production costs of conventional single-basin solar desalination systems. These systems frequently experience significant exergy destruction and thermal instability, which hinder their practical deployment. To address these limitations, this study evaluates the Energy, Exergy, Exergoeconomic, and Environmental (4E) performance of a single-slope solar still enhanced with a porous wool layer and a phase-change material (PCM). Four configurations-Base, Porous, PCM, and the hybrid porous-PCM design-were analyzed to determine their capacity for performance improvement. The results demonstrate that the hybrid porous-PCM configuration provides the most effective performance. The cumulative thermal efficiency increased from 68.54% (Base) to 83.52% (hybrid), while exergy efficiency improved from 4.66% to 5.81%. Daily distilled water production rose from 4.22 L/m2 to 5.91 L/m2, representing a 40.1% enhancement. From an economic perspective, the specific cost of freshwater decreased from 0.019 USD/L in the Base system to 0.013 USD/L in the hybrid design, a 31.6% reduction. Environmentally, avoided CO2 emissions increased from 1.98 kg/m2 & centerdot;day to 3.00 kg/m2 & centerdot;day (a 51.5% improvement). These findings confirm that the combined porous-PCM architecture significantly mitigates exergy destruction and enhances cost-effectiveness, characterizing the integrated 4E framework as a viable approach for sustainable solar desalination in resource-constrained environments.
This article presents the design, modeling, and performance analysis of a high-frequency transformerless resonant DC-DC converter specifically optimized for electric vehicle (EV) charging applications. The proposed converter employs a dual H-bridge architecture with capacitive coupling for galvanic isolation, thereby eliminating bulky magnetic components and significantly improving power density, weight, and cost efficiency. An adaptive phase-shift soft-switching control strategy is introduced to ensure zero-voltage switching (ZVS) and zero-current switching (ZCS) across a wide load range, minimizing switching stress, conduction losses, and electromagnetic interference (EMI). The self-balancing capacitive network maintains a near-zero average voltage across isolation capacitors, reducing voltage stress on semiconductor devices and enhancing long-term reliability. A comprehensive analytical model is developed to describe converter behavior under both continuous and discontinuous conduction modes (CCM and DCM). Theoretical expressions for inductor current, capacitor ripple, and power transfer are derived to optimize resonant operation and control stability. The proposed topology is simulated in MATLAB/Simulink using realistic MOSFET parameters, considering parasitic effects and gate charge characteristics. Simulation results validate that the converter achieves high-efficiency operation (88.579% %) within a 100-500 kHz switching frequency range, delivering up to 6 kW at 500 V output with minimal ripple and rapid transient response. Compared with conventional transformer-based or non-resonant topologies, the proposed system demonstrates 12% reduction in switching losses, 16 dB EMI suppression, and superior soft-switching coverage across 85% of its operational frequency range. These results confirm that the adaptive transformerless resonant converter provides an effective, compact, and reliable solution for next-generation EV charging and renewable energy integration systems.
