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At present, conventional energy production is absent because of the more hazardous gases released into the environment, the high effect on human health, more cost required for maintenance, plus less usefulness for highly populated areas. So, the Renewable Energy Sources are more focused for the present automotive industry application. In this work, the Proton Exchange Membrane Fuel Stack is considered for analyzing the proposed DC–DC converter circuit. The advantages of this fuel stack are high energy density, fast functioning nature, more robustness, and more usefulness for the various water membrane conditions of the fuel stack. However, the disadvantages of the fuel stack are excessive current generation, plus more current conduction losses. So, the wide voltage supply single switch power converter is introduced in this work for optimizing the current production of the fuel stack network. The merits of this converter circuit are high stability, good reliability, low voltage appearing across the switches, plus a uniform power supply. Here, the converter switching pulses are obtained by proposing the Modified Continuous Step Change Adaptive Fuzzy Logic with Grey Wolf Optimization hybrid controller. This controller provides high maximum power extraction efficiency from the fuel stack which is equal to 99.421%. Also, this controller's Maximum Power Point Tracking time is 0.0285 s.

In the recent era, Lithium ion batteries plays a significant role in EV industry due to their high specific energy density, power density, low self-discharge rate, and prolonged lifespan. Modeling the battery precisely and estimating its State of Charge with great precision is essential to improve the performance of the lithium-ion batteries. Though numerous methods has been proposed for estimating the SOC, accurate estimation approach is not proposed yet since all these approaches consider the discrete-time dynamics of the battery. Hence in this proposed approach, the implementation of Thevenin 2RC battery model in conjunction with the Unscented Kalman Bucy Filter (UKBF) for SOC estimation is suggested. Thevenin 2RC battery model is used to captures the nonlinear relationship between the battery’s voltage, current, and SOC. The UKBF is then used to estimate the SOC by fusing the battery model with noisy measurements of the battery’s voltage and current. The UKBF is able to handle the nonlinearity of the battery model and the noise in the measurements, resulting in a more accurate estimate of the SOC by capturing the continuous-time dynamics of the battery. The model is simulated in Matlab Simulink. With similar covariance noise and measurement noise taken into consideration, the battery’s SOC is estimated using the EKF, UKF, and UKBF. The performance comparison indicate that the UKBF approach provides an accurate estimation of the SOC, with a significantly lower RMSE of 0.003276.

The advancement of industrial technology has led to power modules facing challenges such as elevated temperatures, high voltages, and increased frequencies. The conventional epoxy resin (Ep) fails to satisfy the demands of high voltage and superior insulation. This study involved the synthesis of Ep resin/ AgNbO 3 nanocomposites aimed at addressing insulating flaws. AgNbO 3 was modified using the silane coupling agent KH550. The changes in the molecular chain after incorporating of unmodified and modified AgNbO 3 were evaluated through FTIR analysis. The SEM results indicate that following modification, AgNbO 3 particles are evenly distributed within the Ep resin matrix, demonstrating a lack of agglomeration. From thermal conductivity result it was found that modification of AgNbO 3 shows an enhancement of thermal conductivity, attributed to the reduction of interfacial phonon scattering and thermal resistance within the Ep resin. Additionally, the dielectric constant and dielectric loss in composites are greatly impacted by the addition of AgNbO 3 . According to the DC breakdown strength result, the addition of modified AgNbO 3 enhanced the process and caused deeper traps to form inside the bulk matrix. The distribution of trap energy levels is determined by Thermal Stimulated Depolarization Current (TSDC), which indicates that the incorporation of AgNbO 3 results in an increase in trap energy levels. Furthermore, the incorporation of 1 wt% AgNbO 3 particle into Ep resin has been observed to enhance the energy level of deep traps. After AgNbO 3 was added, DC Flashover in both air and vacuum significantly increased in comparison to pure Ep resin.

At present, the availability of nonrenewable sources and their usage for electric vehicle technology is reducing gradually because of their disadvantages are more environmental pollution, direct effect on human health, less reliability, and taking more time to start functioning. So, in this article, the proton exchange membrane fuel cell (PEMFC) is considered for the automotive application because of its advantages quick startup, more power density, more safety to handle, high efficiency, and capability of operating at very low operational temperature conditions. However, the drawback of PEMFC is very difficult to identify the accurate MPP position of the fuel system. Here, the improved variable step genetic algorithm is added with the adaptive neuro-fuzzy inference system for tracking the operational point of the proposed system with high efficiency. These hybrid MPPT controller features are easy to understand, more accurate, have a better dynamic response, and have low design complexity. The evaluated proposed MPPT controller operational efficiency, and settling time of the converter voltage at different fuel stack temperature conditions are 98.7402%, and 0.01607 s respectively. Finally, the boost converter is used in this work to enhance the voltage supply capability of the entire system. The proposed system is investigated by applying the MATLAB tool.

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model’s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.

The development of DC microgrids is reliant on multi-input converters, which offer several advantages, including enhanced DC power generation and consumption efficiency, simplified quality, and stability. This paper describes the development of a multiple input supply based modified SEPIC DC–DC Converter for efficient management of DC microgrid that is powered by two DC sources. Here Multi-Input SEPIC converter offers both versatility in handling output voltage ranges and efficiency in power flow, even under challenging operating conditions like lower duty cycle values. These features contribute to the converter's effectiveness in managing power within a DC microgrid. In this configuration, the DC sources can supply energy to the load together or separately, depending on how the power switches operate. The detailed working states with equivalent circuit diagrams and theoretical waveforms, under steady-state conditions, are shown along with the current direction equations. This paper also demonstrates the typical analysis of large-signal, small-signal, steady-state modeling techniques and detailed design equations. The proposed configuration is validated through the conceptual examination using theoretical and comprehensive MATLAB simulation results. Detailed performance analysis has been done for different cases with various duty ratios. Finally, to show the competitiveness, the multi-input SEPIC topology is compared with similar recent converters.

The present power production companies are working on renewable energy systems because their features are more reliable for the local energy consumers, high continuity in the energy production, and less cost is required for maitainence. In this article, the proton exchange membrane fuel stack (PEMFS) renewable energy is utilized to supply energy to the automotive systems. Here, the PEMFS is selected because of its merits are high energy density, quick system response concerning the source operational temperature, and more suitable for electric vehicle application. However, the PEMFS supplied voltage is completely nonlinear which is solved by utilizing the modified particle swarm optimization with adaptive neuro-fuzzy inference system (MPSO with ANFIS) controller. This hybridization-based maximum power point tracking controller provides more accuracy, high power point identifying speed, best dynamic response at different fuel stack functioning temperature conditions, and easy maitainence. Here, the fuel stack generated current is very high which is optimized by introducing the new DC–DC converter. The advantages of this DC–DC converter are more voltage transformation ratio, low-level voltage stress appearing across the switches, and wide voltage gain. The overall system is investigated by utilizing the MATLAB/Simulink tool.

The conventional MPPT method has drawbacks, such as that under partial shading conditions, several peaks occur and identifying the global peak is difficult. It may converge to a local peak and lead to poor conversion efficiency and tracking efficiency. Implementation of a hybrid algorithm by integrating P&O and metaheuristic algorithms can perform better under partial shading conditions. But the tracking speed is low and the response time is longer. To mitigate the issues mentioned above, a new hybrid algorithm has been suggested that integrates GWO and a modified fast terminal sliding mode controller (MFTSMC). The suggested method with three phase ILBC is incorporated into the PV system. The MATLAB tool is employed to experiment with this study. The performance of GWO-MFTSMC is analysed through MATLAB/ SIMULINK and compared with the performance of ANN-FTSMC and PSO-FTSMC algorithm based MPPT techniques. A hardware prototype is developed and tested for 5 × 200 W solar PV modules with the GWO-MFTSMC algorithm. The proposed method conversion efficiency is 99.72% and 96.15% under simulation and hardware realisation, respectively, which is higher than the ANN-FTSMC and PSO-FTSMC methods.

Plain flaps (PFs) significantly increase camber, enhancing lift and aerodynamic performance when deployed. In Darrieus Vertical Axis Wind Turbines (VAWTs), which perform efficiently in low-speed, turbulent wind conditions, structural modifications like PFs can improve efficiency. This study explores plain flaps with 10-20-degree deflections at different chord lengths to enhance the NACA 2412 aerofoil’s performance. Using Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations and the Shear Stress Transport (SST) k-ω turbulence model, simulations were conducted across high (Re ≈ 2.71 × 10 5 ), medium (Re ≈ 1.35 × 10 5 ), and low (Re ≈ 5.4 × 10 4 ) Reynolds numbers (Re). The 0.7–10 and 0.8–10 configurations significantly improved torque and Cp. At a Tip Speed Ratio (TSR) of 2.5, the 0.8–10 configuration increased the Cp by 19.51% over the flapless NACA 2412 without PF. The 0.7–10 configuration achieved the highest Cp across all TSRs, while a three-blade setup improved Cp by 43% compared to four- and five-blade configurations. The modified blades demonstrated consistent torque gains across all Re, proving the effectiveness of blade shape modifications in enhancing VAWT efficiency, particularly under fluctuating wind conditions, indicating the potential of PF-modified blades in improving small-scale wind turbine performance in varied urban environments.