Akram Bati

This paper presents a comprehensive study on the design and implementation of a Photovoltaic (PV) powered Electric vehicle (EV) charging station for residential parking lots in the UAE. The research focuses on three primary objectives: the design and modeling of the photovoltaic (PV) system, the development of an EV battery model and EV battery charging control system using MATLAB Simulink. This design includes the selection of appropriate PV panels, inverters, and other essential components. The EV charging control system is then designed to manage the charging process, optimizing the battery charging cycles and ensuring efficient energy usage. The control system is tested within the MATLAB Simulink environment to validate its effectiveness and reliability. The results indicate that the proposed PV powered EV charging station can efficiently meet the energy needs of residential EVs while minimizing reliance on the grid. The study demonstrates the feasibility and benefits of integrating renewable energy sources with EV charging infrastructure, contributing to sustainable energy solutions in the UAE.

This paper aims to investigate the suitability of Artificial Intelligence (AI) based algorithms for optimizing the Global Maximum Power Point Tracking (GMPPT) performance in Photovoltaic (PV) systems during partial shading conditions (PSC). The performance of AI based techniques such as Genetic Algorithm (GA), Fuzzy Logic Control (FLC), Partial swarm optimization (PSO), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) will be examined in this paper. A range of PV system configurations, such as 3 panel, 4 panel and 6 panel PV strings and various DC-DC converter topologies, including buck and boost converters, are utilized to test the scalability and variability of the designs. For evaluating the effectiveness of GMMP tracking during PSC a PV system is modelled and simulated using MATLAB SIMULINK. Fuzzy Logic Control and Artificial Neural Network, Adaptive Neuro Fuzzy Inference Systems (ANFIS) based MPPT are implemented using Fuzzy Toolbox, Neural Network Toolbox and ANFIS toolbox in MATLAB. The outcome of the study shows that the GA algorithm exhibits instability and oscillations during partial shading conditions (PSC), failing to track the Global MPP (GMPP) under PSC reliably. The FLC algorithm struggles to track the GMPP during PSC accurately. On the other side, PSO demonstrates a good tracking performance, achieving a GMPP tracking efficiency of 90.23% on average, though it does not track under certain PSCs the average MPPT tracking efficiency of ANN is 77.71% for the six cases. However, ANN is unable to track GMPP and is unstable during PSC. Out of six partial shading tests conducted, ANFIS MPPT was able to track the GMPP in three specific PSC scenarios.

This work presents a model of a Class E-2 converter for wireless medium power transfer applications. The converter operates at frequency of 200 kHz and consists of an inductive link with its primary coil driven by a class E inverter and the secondary coil with voltage driven class E synchronous rectifier. A 7th order linear time invariant LTI state-space model is used to obtain the eigenvalues of the system for the four modes caused by the operation of the converter switches. A participation factor for the four modes is used to find the actual operating point dominant poles for system response. A dynamic analysis is carried out to investigate the effect of changing separation distance between the two coils based on converter performance and the changes required of some circuit parameters to achieve optimum efficiency and stability. The results show an excellent achievement in terms of efficiency (90-98%) and maintaining constant output voltage by the use of a PI controller with dynamical change of capacitors in the inverter.

This work presents a model of a Class E2 converter for wireless medium power transfer applications. The converter operates at frequency of 200 kHz and consists of an inductive link with its primary coil driven by a class E inverter and the secondary coil with voltage driven class E synchronous rectifier. A 7th order linear time invariant LTI state-space model is used to obtain the eigenvalues of the system for the four modes caused by the operation of the converter switches. A participation factor for the four modes is used to find the actual operating point dominant poles for system response. A dynamic analysis is carried out to investigate the effect of changing separation distance between the two coils based on converter performance and the changes required of some circuit parameters to achieve optimum efficiency and stability. The results show an excellent achievement in terms of efficiency (90-98%) and maintaining constant output voltage by the use of a PI controller with dynamical change of capacitors in the inverter.