Hydrogen is acknowledged as a potential and appealing energy carrier for decarbonizing the sectors that contribute to global warming, such as power generation, industries, and transportation. Many people are interested in employing low-carbon sources of energy to produce hydrogen by using water electrolysis. Additionally, the intermittency of renewable energy supplies, such as wind and solar, makes electricity generation less predictable, potentially leading to power network incompatibilities. Hence, hydrogen generation and storage can offer a solution by enhancing system flexibility. Hydrogen saved as compressed gas could be turned back into energy or utilized as a feedstock for manufacturing, building heating, and automobile fuel. This work identified many hydrogen production strategies, storage methods, and energy management strategies in the hybrid microgrid (HMG). This paper discusses a case study of a HMG system that uses hydrogen as one of the main energy sources together with a solar panel and wind turbine (WT). The bidirectional AC-DC converter (BAC) is designed for HMGs to maintain power and voltage balance between the DC and AC grids. This study offers a control approach based on an analysis of the BAC’s main circuit that not only accomplishes the function of bidirectional power conversion, but also facilitates smooth renewable energy integration. While implementing the hydrogen-based HMG, the developed control technique reduces the reactive power in linear and non-linear (NL) loads by 90.3% and 89.4%.

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.

In Electric Vehicle (EV) application, voltage conversion is significant to obtain the desired operating voltage from the source voltage. A conventional boost converter can handle such applications, but it may add losses throughout the conversion process. This work focuses on the design and implementation of a multi device Interleaved DC‐DC converter with greater voltage gain, lower voltage stress across the switch, and improved efficiency when compared to the standard Boost converter and conventional Interleaved Converter. The suggested converter has three times the voltage gain of a standard Boost DC‐DC converter. These converters are used in applications that demand a constant DC voltage, such as electric vehicles. The proposed converter's mathematical modelling and modes of operation are discussed. The proposed DC‐DC converter's feasibility is validated using real‐time simulation (OPAL‐RT), and the results are presented in detail.

The synchronous integration of numerous input and output loads is possible with multi-input (MI) and multi-output (MO) DC–DC converters. In this paper, the non-isolated DC–DC converter described, which has a high step-up capability and multiple ports for outputs and inputs for energy storage system (ESS) applications. The voltage level of the converter is changeable. The capacity to provide the large voltage increases with a low duty cycle portion, the ease with which each duty cycle can be controlled, and minimal power losses are all advantages of the proposed design. The proposed system offers advantages for applications requiring energy storage. In the continuous conduction mode (CCM), the operation principles, steady-state evaluation, and extracting of the voltage and current coefficients are performed. The supply sources can be inserted or withdrawn without causing a cross-regulation issue in the proposed converter. Ultimately, the functionality of the proposed structure is examined using simulation and the laboratory prototype that has been implemented. The proposed converter achieved 94.3% efficiency at maximum power. In addition, the proposed converter attained minimum losses with a difference of 28.5 W when compared to a conventional converter.

In order to enhance the power transformation stage’s power transfer capabilities and efficiency, in this article, improved three-port two step-up single-ended primary-inductor converters (SEPIC) converter fed (Photovoltaic )PV- Hybrid Electric Vehicle was proposed. In comparison to the standard single-stage SEPIC, the proposed converter accepts a wider range of input voltages. The proposed three-port converter uses a multiple-winding high-frequency transformer (HFT) to integrate the dual sources and provide greater voltage gain with lesser elements. Furthermore, by predicting the drive torque need, the power management algorithm (PMA) included with the proposed PV-hybrid electric vehicle (HEV) minimizes the drive motor’s power consumption. An experimental model with a power output of 6 kW and a voltage range of 12 to 600 volts has been created and tested. The designed model has 94.11% efficiency.

The induction motor (IM) defect diagnosis has been an important field of research in recent years. The development in control circuits for IM has piqued the interest of industrialists and researchers. This paper presents a method for detecting and quantifying broken rotor bar (BRB) faults via wavelets and energy Eigen value (EEV) estimation in voltage/frequency control-fed IM. The fast Fourier transform (FFT) extracts the signal’s amplitude and frequency components, while the discrete wavelet transform (DWT) decomposes it. In this paper, the energy estimation for each level of breakdown and the method to overcome the diagnose faults are explained. The EEV of the motor current of the signal determines the fault’s severity and provides a better method for identifying the faults. The usage of a single current sensor is a gain of this technology. With a fluctuating load, we can identify the issue and the number of broken bars via online. After processing of DWT, the faulty BRB’s stator current signal is suppressed to 91% in amplitude when compared to existing techniques. Simulation and experimental results have proved that the proposed method’s stability, durability, and resilience.