Faris Mohammed Ali

Dual linear polarized cross-slot patch antenna is a good candidate for modern base station antennas in sub-6 GHz fifth-generation (5G) cellular communications due to its low profile, low cost and moderate bandwidth. The best-obtained results of the single antenna design demonstrate a wide impedance bandwidth of 19.72% (3.2–3.9 GHz) for VSWR < 1.4, isolation of less than 50 dB between both ports, and a high gain of 10.5 dBi. The designed dual-polarized antenna was placed in a two-element array that is formed once with and once without via pins in two cases: 1- vertical/horizontal polarization. 2- ±45° slant polarization. The radiation pattern in both incidents has been studied and reported. In case one, the results show that the mutual coupling deteriorates the radiation pattern with the side lobe of around -12.6 dB at 3.4 GHz. However, the sidelobe level is better at the level of -19 dB, although not acceptable in the case of square vias. The S11 is good from 3.25 GHz to 3.7 GHz (450 MHz). Similarly, the gain is 13.5 dBi without a via and with a via. A great result: an increase from 10.5 dBi for a single element to 13.5 dBi for a two-antenna array. In case two, the best result obtained in this scenario is that the S11 is below –10 dB from 3.2 to 3.9 (700 MHz) without the use of vias. The 45-degree slant radiation patterns of the antenna, which are plotted in both perpendicular radiations. Both co-polar and cross-polar patterns are shown and are overlaid. The VSWR is less than 1.5 in all cases. The results obtained and the compatibility achieved in the direction allow for the possibility of using this antenna in many applications in modern communications systems, such as advanced antenna systems (AAS) and radars.

Electric Vehicle (EV) charging use is going up solar installations on rooftops and on a large scale in power plants are increasing consequently. Therefore, a Battery Management System (BMS), which is the system for managing battery storage, was used to control the solar energy generation to coordinate charging. Such a connection gives the BMS the opportunity to modify charging/discharging patterns with the help of solar energy predictions and prices of electricity from the grid. Moreover, the battery is being protected from aging with the BMS to monitor the state of charge/health of the battery. The BMS protects the Li-ion batteries against overcharging, discharging, heating and managing the used energy. In this paper, a comprehensive review of solar-powered EV-based BMS methods is presented. moreover, different BMS methods are applied to the Li-ion batteries, including classical methods such as Coulomb Counting (CC) andopen-circuit voltage, improved Kalman Filter (KF) methods, and Artificial Intelligence (AI) methods. Moreover, estimation of the battery’s SOC is very important to protect the battery cells from overcharging/discharging, and Implementing this can enhance the battery’s performance and extend its lifetime. The most used methods in the literature for estimation SOC are the KF and extended KF methods. Although these methods have more advantages, such as simplicity and low-cost implementation, they suffer from low response and the computationally intensive. Unlike the BMS-based AI methods, it provides more benefits in managing the Li-ion batteries of EVs, including high degree of accuracy, establishing the discharge voltage limit, ensuring the protection of battery cells, and minimizing the time required. As for future scope, there is a possibility of developing the coordination of vehicle to grid or grid to vehicle (V2G/V2G) operation using adaptive BMS and AI strategies to improve the performance and enhance the reliability.

Hybrid renewable energy sources are combined in microgrids to feed rural areas and remote locations where grid power is unavailable. This study aims to feed remote areas and newly established projects or factories far from the power source using renewable energy sources to continuously satisfy various load demands and supply power. A smart microgrid design is proposed for a hybrid system that includes solar energy with an artificial neural network (ANN), a wind turbine, an energy storage system that stores energy in a photovoltaic battery and uses it when needed, and an emergency diesel generator. To verify the effectiveness of the proposed approach, a mobile generation station consisting of diesel generators, solar PV systems, wind turbines and an energy storage system that stores energy in a bank of batteries is designed as an additional service source in MATLAB Simulink. The model has a capacity of 1MW and can handle loads of up to 800KW. With ANN, a stable output voltage solar without ripples was obtained. Load priority algorithm (LPA) was also used to coordinate between three loads: High, Medium, and Low Load priority, and the actual diamond for each load was achieved. The first load with the highest priority was fed 100%, and the remaining loads were fed to cover their needs, proving the design's validity. It demonstrates the effectiveness of the proposed solution in mitigating power outages. The station can serve all loads by using renewable energy sources (RES) to meet the energy demands of the loads. Las fuentes de energía renovables híbridas se combinan en microrredes para alimentar zonas rurales y lugares remotos en los que no se dispone de energía de red. El objetivo de este estudio es alimentar zonas remotas y proyectos o fábricas de nueva creación alejados de la fuente de alimentación utilizando fuentes de energía renovables para satisfacer de forma continua diversas demandas de carga y suministrar energía. Se propone un diseño de microrred inteligente para un sistema híbrido que incluye energía solar con una red neuronal artificial (RNA), un aerogenerador, un sistema de almacenamiento de energía que almacena energía en una batería fotovoltaica y la utiliza cuando es necesario, y un generador diésel de emergencia. Para comprobar la eficacia del planteamiento propuesto, se diseña en MATLAB Simulink una estación de generación móvil compuesta por generadores diésel, sistemas fotovoltaicos solares, turbinas eólicas y un sistema de almacenamiento de energía que almacena energía en un banco de baterías como fuente de servicio adicional. El modelo tiene una capacidad de 1 MW y puede manejar cargas de hasta 800 kW. Con la RNA se obtuvo una tensión de salida solar estable y sin ondulaciones. También se utilizó el algoritmo de prioridad de carga (LPA) para coordinar las tres cargas: Alta, Media y Baja prioridad de carga, y el diamante real para cada carga se logró. La primera carga con mayor prioridad se alimentó al 100%, y el resto de cargas se alimentaron para cubrir sus necesidades, demostrando la validez del diseño. Esto demuestra la eficacia de la solución propuesta para mitigar los cortes de energía. La estación puede dar servicio a todas las cargas utilizando fuentes de energía renovables (FER) para cubrir las demandas energéticas de las cargas.

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