Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters’ estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of $7.7547\times 10^{-4}$ , $7.64801\times 10^{-4}$ , and $7.59756 \times 10^{-4}$ for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

Numerous recent innovations have been achieved with the goal of enhancing electric vehicles and the parts that go into them, particularly in the areas of managing energy, battery design and optimization, and autonomous driving. This promotes a more effective and sustainable eco-system and helps to build the next generation of electric car technology. This study offers insights into the most recent research and advancements in electric vehicles (EVs), as well as new, innovative, and promising technologies based on scientific data and facts associated with e-mobility from a technological standpoint, which may be achievable by 2030. Appropriate modeling and design strategies, including digital twins with connected Internet of Things (IoT), are discussed in this study. Vehicles with autonomous features have the potential to increase safety on roads, increase driving economy, and provide drivers more time to focus on other duties thanks to the Internet of Things idea. The enabling technology that entails a car moving out of a parking spot, traveling along a long highway, and then parking at the destination is also covered in this article. The development of autonomous vehicles depends on the data obtained for deployment in actual road conditions. There are also research gaps and proposals for autonomous, intelligent vehicles. One of the many social concerns that are described is the cause of an accident with an autonomous car. A smart device that can spot strange driving behavior and prevent accidents is briefly discussed. In addition, all EV-related fields are covered, including the likely technical challenges and knowledge gaps in each one, from in-depth battery material sciences through power electronics and powertrain engineering to market assessments and environmental assessments.

This research work addresses the challenge of controlling a wind energy conversion system (WECS) connected to the grid, which incorporates a battery energy storage system and operates under varying real wind speed profiles. The system comprises a Doubly-Fed Induction Generator (DFIG) connected to the grid through an AC/DC/AC converter, along with a Li-ion battery pack storage unit linked to the Back-to-Back converter DC via a DC/DC converter. The study highlights that internal parametric variations and the non-linear behavior of the wind turbine negatively affect the energy quality produced, as well as the battery charging performance and its overall lifespan. To overcome these issues, the research proposes a robust control strategy based on Integral action Sliding Mode Control (ISMC) to independently manage the powers of the WECS, particularly the DFIG, to enhance dynamic performance. Furthermore, effective battery charging and discharging controllers are crucial for efficiently distributing the generated power to both the grid and the storage unit, ensuring proper energy extraction and battery charging/discharging. Depending on the battery’s State of Charge (SOC), the available extracted energy, and the power injected into the grid, two current regulation modes are employed: buck charging mode and boost discharging mode. A storage system energy management algorithm is then utilized to determine the appropriate charging mode, ensuring battery safety. To validate the system’s performance, a 1.5 MW wind power conversion system is tested using Matlab/Simulink simulations. The results demonstrate the robustness and efficiency of the proposed control approach in enhancing the overall system’s performance.

Integrating wind power plants (WPPs) into power systems are increasing dramatically now a day. However, the dynamic performance of power systems will be affected by the large penetration level of such renewable sources of energy. From this context power system operators and transmission system operators have put regulation rules to keep pushing wind power plants to safeguard limits that keep power system more stable and reliable. One of these rules is providing a low voltage ride through (LVRT) for wind farms without disconnecting it from the power system. The current paper implements the STATCOM as a LVRT for a 9 MW wind farm connected to the grid through transmission system of 120 kV. For enhancing the dynamic performance of STATCOM, two types of optimization methodologies: ant colony (ACO) and particle swarm optimization (PSO), are proposed to fine tune the coefficients of PI controllers to optimally manage the STATCOM dynamics.

Motor Current Signature Analysis (MCSA) is a popular method for the detection of faults in electric motor drives, particularly in Induction Machines (IMs). For Bearing Defects (BDs), which are very much related to the rotational frequency, it is important to maintain the speed at a target reference value in order to distinguish and locate the different BDs. This can be achieved by using a powerful control such as the Direct Torque Control (DTC), but this control causes the variation of the supply frequency and the current signal to become non-stationary, so the integration of advanced signal processing methods becomes necessary by using a suitable filter to handle the frequency content depending on the BDs, such as the Hilbert filter. This paper aims to adopt the Hilbert Transform (HT) for extracting the signature of the faults from the stator current envelope to detect the different BDs in the IMs when they are controlled by an intelligent DTC control driven by Artificial Neural Networks (ANN-DTC). This ANN-DTC control is a shaping factor rather than a disturbing one, which contributes with the Hilbert filter to the diagnosis of BDs. This technique is tested for the four locations of BDs: the inner ring, the outer ring, the ball, and the bearing cage in different operating situations without control and with conventional DTC and ANN-DTC controls. Thus, detecting the location of the defect exactly at an early stage contributes to achieving maintenance in a fairly short time. The performance of the chosen approach lies in minimizing the electromagnetic torque ripples as a result of the control and increase of the amplitudes of the spectra related to BDs compared to other harmonics. This performance is verified in the MATLAB/SIMULINK environment.