Battery management systems (BMS) are becoming essential for all types of electric vehicles using battery packs. Various factors, such as battery temperature and balance, directly affect the life, safety, and efficiency of batteries used in vehicles. For security and robustness, these factors should be monitored and adjusted instantly. Today, battery management systems are constantly being developed using different production methods and algorithms. In the studies, calculations are made by measuring parameters such as temperature, current, current balance, load status, and health status of the battery cells, and the control of the battery group is provided with these calculations. Instant and continuous measurement and processing of all these data and the creation of a control algorithm according to the calculation result are possible with the use of powerful processors. FPGA is a processor that can provide the speed and functionality required for BMS. In the battery management system, the FPGA is responsible for receiving and processing all signals from the battery cells and producing results. It instantly processes the data from temperature, current, and voltage sensors and applies the control stage required for balancing. In addition, the charge and discharge capacity of the battery is calculated by instantly measuring the state of charge (SOC). SOC is of great importance in the battery management system to ensure the safety of the battery pack. Therefore, the SOC needs to be estimated accurately and in real-time. Thanks to its parallel processing capability, the FPGA can simultaneously read data from the sensors and perform related calculations. In this study, a versatile system design with real-time, high computational speed for BMS was carried out on FPGA. The voltage and current of an experimental battery based on the embedded system were monitored in real time in a simulation environment. Experimental results show that the instantaneous SOC estimation is successful, and the system returns instant results to the incoming sensor data. The use of FPGA as a management unit will provide significant advantages in BMS with its high operating speed, real-time monitoring, low power consumption, and re-programmability.

The robotic welding process is widely used in many industry sectors, and its use in production lines is becoming more common day by day. Obtaining a smooth weld seam in robot welding depends on the geometric structure of the welding path and the stability of the control loop. However, the weld path and the weld gap are usually not fixed, and their change negatively affects the automatic control. Programming the complex welding path by the operator may take more time than executing the task for some welding jobs. In addition, the variable weld gap negatively affects the weld quality in the constant control loop. This study proposes a system that provides a real-time definition of the weld path and its geometry on the embedded system to address this issue. The weld path image is captured using a camera, and the weld path is determined by image processing techniques using the embedded Linux operating system running on system-on-chip (SoC) hardware. The images captured through the Hard Processor System (HPS) unit are stored in memory, processed in the FPGA unit, and output by the HPS unit. Unprocessed SoC images and measurement images of weld pieces are presented with their values. When the values obtained from the processed weld path image are compared to manually measured path values, it is seen that the proposed system produces successful results.

Epileptic attacks can be caused by irregularities in the electrical activities of the brain. Electroencephalography (EEG) data demonstrating electrical activity in the brain play an important role in the diagnosis and classification of epileptic attacks and epilepsy disease. This study describes a method for detecting epileptic attacks using various machine learning methods and EEG features obtained with the Discrete Wavelet Transform (ADD). In the study, an EEG dataset consisting of five separate clusters from healthy and sick individuals was used, and the classification success between these conditions was examined separately. Support Vector Machine (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbor (k-NN), Decision Trees (Tree), Random Forest, and Naive Bayes machine learning methods, which are widely used in classification, were used. In addition, comparisons were made with various windowing and overlap ratios. As a result, classification successes, as well as optimal windowing and overlap ratios were determined for various EEG clusters in the dataset.

ADAPTIF BULANIK MANTIK KONTROLU ILE MAKSIMUM GUC NOKTASI IZLEYICI TASARIMI VE GERCEKLEMESI Ozet Enerji ihtiyacinin teknolojik gelismelere ve populasyona bagli olarak gunden gune artmasi, insanlari alternatif enerji kaynaklari bulmaya yonlendirmektedir. Alternatif enerji kaynaklari arasinda en cok kullanilan kaynak, farkli enerji turlerine donusturulebilmesi ve kolay erisilebilmesi nedeniyle gunes enerjisidir. Ulkemiz, gunes enerjisinin kaynak olarak kullanilmasi acisindan elverisli bir konuma sahiptir. Gunes enerjisinden elektrik enerjisinin uretilebilmesi icin fotovoltaik paneller kullanilmaktadir. Bu noktada onemli olan, fotovoltaik paneller yardimiyla kaynaktan alinan enerjiden mumkun oldugunca cok verim saglayabilmektir. Yuksek seviyede verimin elde edebilmesi icin, gunes isinlarinin panele mumkun oldugunca dik ve uzun sure ulasmasi gerekmektedir. Bu amacla da panelin gunesi izledigi sistemler gelistirilmistir. Gunesi takip sistemleri tek veya iki eksenli olarak tasarlanmaktadir. Bu calismanin amaci, adaptif bulanik mantik kontrolu kullanarak, gunes takip sistemlerinin kontrolunu saglamak ve gunes enerjisinden elde edilen verimin artirilmasini saglamaktir. Bu amac dogrultusunda, gunes enerjisinden maksimum verimin elde edilebilmesi icin, adaptif bulanik mantik kontrollu bir maksimum guc noktasi izleyici (MPPT) sistemi tasarlanarak gerceklestirilmistir. Anahtar Kelimeler: Adaptif bulanik kontrol, Bulanik mantik, Fotovoltaik panel, MPPT, Gunes takip sistemi DESIGN AND IMPLEMENTATION OF MAXIMUM POWER POINT TRACKER WITH ADAPTIVE FUZZY LOGIC CONTROL Abstract The increase in energy demand due to technological developments and population, leads people to find alternative energy sources. Among the alternative energy sources, solar energy is the most widely used source because of its’ transformation to different energy types and easy access. Our country has a favorable position in terms of using solar energy as a source. Photovoltaic panels are used to generate electrical energy from solar energy. At this point it is important to provide as much efficiency as possible from the energy taken from the source with the help of photovoltaic panels. In order to achieve a high level of efficiency, the sun's rays must reach the panel as perpendicular and as long as possible. For this purpose, systems which the panel tracks the sun have been developed. Solar tracking systems are designed as single axis or multi axis. The aim of this study is to provide control of solar tracking systems and to increase the efficiency of solar energy by using adaptive fuzzy logic control. For this purpose, a maximum power point tracker (MPPT) system with adaptive fuzzy logic control has been designed and implemented in order to obtain maximum efficiency from solar energy. Keywords: Adaptive fuzzy control, Fuzzy logic, Photovoltaic panel, MPPT, Solar tracking system