DC-DC converters are commonly used in many fields such as renewable energy sources, electrical vehicles, power supplies, battery chargers and fuel cell applications. These converters are used for purposes such as voltage conditioning, regulation of their outputs, obtaining maximum power and providing isolation. In practice, various criteria such as cost, performance, efficiency and size are considered while converter topology is determined. Filter components (L-C) which are used in DC-DC converter determine the converter size. In this study, an interleaved quadratic boost converter is designed for fuel cell applications and different inductor designs are investigated for the proposed quadratic boost converter. Four different inductor models are designed and simulation studies of the interleaved quadratic boost converter with these inductor models are carried out. Effects of the designed inductors on converter output waveform quality, total weight and volume, and converter efficiency are investigated and obtained results are compared. It is seen from obtained results that, the coupled inductor improves performance and efficiency of the interleaved quadratic boost converter.

DC-DC converter circuits are topologies commonly used in power electronics applications such as renewable energy sources, electric vehicles, uninterruptible power supplies and DC transmission systems. The most important factors affecting efficiency and thus performance is the choice of the power semiconductor switching element as well as the circuit design and types of these topologies. In this context, power semiconductors are determined according to the switching frequency and current-voltage parameters. However, due to other operating modes of the circuit and load variation during the power conversion, the losses of the switching elements do not remain constant. In this study, a parametric simulation is performed in a conventional DC-DC boost converter circuit using the parameters related to the Insulated-Gate Bipolar Transistor (IGBT) power-switching element selected at a certain current-voltage capacity. These parameters are switching frequency, duty ratio and load change of the converter. Finally, using the data obtained, the loss of switching losses are estimated by the Multilayer Perceptron (MLP), Support Vector Machine (SVM), K- Nearest Neighbors (KNN) and Random Forest (RF) Machine Learning (ML) techniques.

Recently, with the developing environmental awareness, electric vehicles are increasing even more. For this reason, different searches have emerged to solve the problems related to meeting the energy needs of electric vehicles and charging their batteries quickly and reliably. One of these ideas is wireless power transfer (WPT) battery charging systems, which researchers have focused on for the past two decades. In this study, a wireless charging station that can be used to charge the batteries of electric vehicles is designed and examined by applying it to a prototype vehicle. Also, it is examined that the designed system can be adapted with renewable energy sources (such as solar energy) independently of a local energy source. It is aimed with the WPT prototype to realize a more efficient system for the 10 W power level and 86 kHz. The electromagnetic modelling of WPT is designed using ANSYS-Electronics/Maxwell software. Ultimately, the power electronics circuit performance of this system was analyzed with ANSYS Electronics / Simplorer software for co-simulation.

In this study, the low magnitude of 48 V DC voltage obtained from fuel cell (FC) is boosted in the output voltage range of 500-530 V by using a double cascaded boost converter (DCBC) circuit. The power electronic circuit of the battery charging system has been simulated with the parametric analysis and there have been obtained output voltage ripple about 1295 values at the converter output according to the load variation and the different switching frequencies. After that, the output voltage ripple is estimated with Artificial Neural Network (ANN) for adaptive frequency control depending on the load variation. Thus, the output voltage ripple is taught to the system according to the nonlinear load change and it can be kept at a safe level with the frequency control. In such a circuit, the parametric simulation time takes about a week according to the computational performance. The ANN model eliminate the mathematical procedures and time consuming for circuit simulations. The results obtained with the ANN model are in harmony with the simulation results and these results show that it can be successfully used to estimate the output voltage ripple of the proposed DC-DC boost converter. In this way, the ANN model has been developed with a very high accuracy (<0.5%) in that the charging voltage ripple of Li-Ion batteries can be kept at safe levels.

Solar and wind based renewable energy sources are highly variable according to unstable energy sources due to variable meteorological conditions. Providing a stable DC bus voltage to the inverter’s input by eliminating voltage fluctuations in the generated electrical energy is the main concern of researchers and is highly sensitive in the integration of distributed energy sources. In this study, in order to provide stable and sustainable output voltage at the output of a DC/DC converter, parametric simulation studies are carried out according to different input voltage, duty ratio, and switching frequency values of a DC/DC power converter circuit in a system fed with a solar energy source. Using this dataset obtained from a solar energy source, a parameter estimation in the smart grid structure is studied using artificial hybrid prediction intelligence techniques. Grey wolf and marine predators optimization algorithms are integrated to the multilayer perceptron in the stage of developing the hybrid prediction models. Then, the accuracy of the prediction processes is tested and verified with the power electronics circuit software. Thus, a data analysis approach based on parametric simulation studies in smart grid structures is reported in order to give clear ideas to researchers before designing a prototype.