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.

Maximum Power Point Tracking technologies are being used in traditional PV system charge controllers to enhance the power conversion efficiency. An MPPT controller will ensure power extracted from the PV panels during varying climatic conditions is always maximum. This will ensure that maximum power is flowing between the panel and load. As both Temperature and Irradiation levels vary during the day, maximum power point trackers are an inevitable component in a PV system. As solar energy holds a major share in renewable energy in the world market, an improvement in MPPT technique makes the efficiency of the PV system increase and in turn cost reduction possible. However, the efficiency of conventional MPPT Techniques suffers from failing in tracking MPPT at fast varying climatic conditions and falling in local maxima of maximum power point than global maxima. The issues of stability, tracking speed, and accuracy can be solved using intelligent MPPT techniques methods based on soft computing tools: Therefore, this paper aims to study and provide a comparative analysis of two AI-based MPPT techniques such as ANN and ANFIS. The MPPT techniques considered in this study are ANN and ANFIS. Performance evaluation is carried out using MATLAB Simulation. Experimental results indicate that the two methods ANN and ANFIS are more efficient than conventional MPPT techniques due to its capability to avoid local MPP and partially shaded conditions.
Maximum Power Point Tracking technologies are being used in traditional PV system charge controllers to enhance the power conversion efficiency. An MPPT controller will ensure power extracted from the PV panels during varying climatic conditions is always maximum. This will ensure that maximum power is flowing between the panel and load. As both Temperature and Irradiation levels vary during the day, maximum power point trackers are an inevitable component in a PV system. The renewable energy source has a vital role in supplying sustainable power to meet the rising electricity demands. However, the PV system performance heavily depends on environmental conditions. This, in turn, causes efficiency to be less and in turn higher cost. For maximum power to be transferred from the PV panels under varying climatic conditions PV systems should operate at Maximum Power Point.

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.

A dynamic response analysis model of a Class E2 converter for wireless power transfer applications is presented. The converter operates at 200 kHz and consists of an induction link with its primary coil driven by a class E inverter and the secondary coil with a voltage-driven class E synchronous rectifier. A 7th order linear time invariant state-space model is used to obtain the eigenvalues of the system for the four modes resulting from the operation of the converter switches. A participation factor for the four modes is used to find the actual operating point dominant poles for the system response. A dynamic analysis is carried out to investigate the effect of changing the 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 good performance in terms of efficiency (90-98%) and maintenance of constant output voltage with dynamic change of capacitance in the inverter. An experiment with coils of dimension of 53× 43× 6 mm3 operating at a resonance frequency of 200 kHz, was created to verify the proposed mathematical model and both were found to be in excellent agreement.

The limitations of sensorless control of permanent magnet synchronous machines (PMSMs) are discussed and a viable solution is proposed. The main concept of sensorless control of drives relies on additional information given by the machine during its normal operation. This information provided by the machine is essentially the back-electro motive force and the variance of the stator inductivity, which are dependent on the rotor position. Several approaches and methods have discussed these problems, and in most cases they are not avoidable and that some methods work better on certain speeds of the drives. The direct flux control (DFC) method to combat the above problems at all speeds is presented. The flux linkage signal which contains the necessary information about the rotor position can be measured between the neutral point of a PMSM and an artificial one. The mathematical derivation and the observations from the experiments show that this signal contains a second and a fourth harmonic, which can be used to calculate the rotor position. Furthermore, the limitations of implementing DFC are also addressed.

This paper presents a multi-period unit commitment active and reactive power optimal management for microgrids under different market policies. The overall optimization problem is formulated by mixed integer quadratic programming by taking into considerations the environmental costs and the battery degradation cost against a comprehensive set of constraints. A typical low-voltage microgrid, which comprises of distributed generators, renewable energy resources, storage battery, and varieties of loads, are employed to implement and examine the proposed approach. The microgrid is comprehensively investigated with both grid-connected and isolated modes under minimum operation and emission cost and maximum overall profit policies. The results have revealed that the charging and discharging operations of storage battery typically reduce the total operation and emission costs and hence maximize overall profit, even considering the battery degradation.