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Öğe Bataryaların kondisyonlarını izleyerek yapay sinir ağları ile batarya türü ve şarj durumu tahmini(Karabük Üniversitesi, 2016) Soylu, Emel; Bayır, RaifBataryalar elektrik enerjisini elektrokimyasal enerjiye dönüştürerek depolayabilen ve istendiği anda depoladığı enerjiyi elektrik enerjisi olarak verebilen enerji depolama sistemleridir. Taşınabilir elektronik cihazların yaygınlaşması şarj edilebilir bataryaların yaygınlaşmasına ve batarya teknolojisinin gelişmesine yol açmıştır. Bu nedenle bataryalar günlük hayatın vazgeçilmezleri haline gelmiştir. Bu çalışmada şarj edilebilir bataryaların gerçek zamanlı olarak kondisyonları izlenerek yapay sinir ağları ile şarj durumu ve tür tahmini yapılmaktadır. Batarya hücreleri üzerinde elektriksel ölçüm deneyleri yapmak için bir ölçüm düzeneği ve ölçülen verileri analiz etmek, bataryaların kondisyonlarını izlemek için bir kullanıcı arayüzü geliştirilmiştir. Çalışmada beş farklı batarya türünden güç değerleri birbirine denk bataryalar üzerinde deneysel çalışmalar yapılmıştır. İleri beslemeli yapay sinir ağı, kademeli bağlantılı yapay sinir ağı ve radyal tabanlı ağların batarya türü ve batarya şarj durumu tahmin edilerek ağların performansları karşılaştırılmıştır. Yapay sinir ağları ile batarya kondisyon izleme, batarya türü ve batarya şarj durumu belirlemede başarılı sonuçlar elde edilmiştir. Kullanılan yöntemler arasında batarya türü belirlemede ileri beslemeli yapay sinir ağı, batarya şarj durumu belirlemede kademeli bağlantılı yapay sinir ağının daha başarılı sonuçlar verdiği görülmüştür. Bu ölçüm düzeneği ve yazılım ile bataryaların gerçek zamanlı olarak da kondisyonları izlenebilir, türleri ve şarj durumları tahmin edilebilir.Geliştirilen yapay sinir ağı modelleri gömülü sistemlerde kullanılarak prototip sistem tasarımı yapılabilir.Öğe Design and Implementation of SOC Prediction for a Li-Ion Battery Pack in an Electric Car with an Embedded System(Mdpi, 2017) Soylu, Emel; Soylu, Tuncay; Bayir, RaifLi-Ion batteries are widely preferred in electric vehicles. The charge status of batteries is a critical evaluation issue, and many researchers are studying in this area. State of charge gives information about how much longer the battery can be used and when the charging process will be cut off. Incorrect predictions may cause overcharging or over-discharging of the battery. In this study, a low-cost embedded system is used to determine the state of charge of an electric car. A Li-Ion battery cell is trained using a feed-forward neural network via Matlab/Neural Network Toolbox. The trained cell is adapted to the whole battery pack of the electric car and embedded via Matlab/Simulink to a low-cost microcontroller that proposed a system in real-time. The experimental results indicated that accurate robust estimation results could be obtained by the proposed system.Öğe Embedded System Design and Implementation of an Intelligent Electronic Differential System for Electric Vehicles(Science & Information Sai Organization Ltd, 2017) Uysal, Ali; Soylu, EmelThis paper presents an experimental study of the electronic differential system with four-wheel, dual-rear in wheel motor independently driven an electric vehicle. It is worth bearing in mind that the electronic differential is a new technology used in electric vehicle technology and provides better balancing in curved paths. In addition, it is more lightweight than the mechanical differential and can be controlled by a single controller. In this study, intelligently supervised electronic differential design and control is carried out for electric vehicles. Embedded system is used to provide motor control with a fuzzy logic controller. High accuracy is obtained from experimental study.Öğe Fuzzy proportional-integral speed control of switched reluctance motor with MATLAB/Simulink and programmable logic controller communication(Sage Publications Ltd, 2019) Uysal, Ali; Gokay, Serdar; Soylu, Emel; Soylu, Tuncay; Caska, SerkanIn this study, the auto-tuning proportional-integral controller is used to control the speed of a switched reluctance motor. The control algorithm is executed by the programmable logic controller. The proportional integral gains are determined via fuzzy logic. Fuzzy logic is executed on a separate computer via MATLAB/Simulink software. The data exchange between the programmable logic controller and MATLAB/Simulink is done with object linking embedding/component for the process. The fuzzy proportional integral control algorithm is compared with the conventional proportional integral controller. We reduced the load on the programmable logic controller via executing fuzzy logic in a separate computer and at the same time eliminated the disadvantages of the conventional proportional-integral controller. With the proposed method, the engine reached the reference speed value in a short time and the overshoots were eliminated in variable conditions such as different load and different speed conditions.Öğe Measurement of Electrical Conditions of Rechargeable Batteries(Sage Publications Ltd, 2016) Soylu, Emel; Bayir, RaifBatteries are used in a wide area, from mobile phones to electric vehicles. Batteries are used in electric cars, satellites and space systems, communications systems, defense systems, renewable energy sources, and many different application areas. Condition monitoring of batteries and storing measurement data are very important issues. Manufacturers, researchers, maintenance services, and so on use special software and database for viewing and saving measurement data. It is important to measure the data with high accuracy, view with graphs, and save these data systematically. In this study, software is developed and a database is designed to monitor the battery conditions online. This software is developed in C# programming language, and SQL Server is used as database. Current, voltage, resistance, power, temperature of the battery, and ambient temperature are measurement values. Some battery experiments take very long time and someone should wait near the test system to prevent dangers such as explosion and fire. A wide variety of electrical battery experiments can be done without waiting next to the test system with the proposed software and database.Öğe Real Time Determination of Rechargeable Batteries' Type and the State of Charge via Cascade Correlation Neural Network(Kaunas Univ Technology, 2018) Bayir, Raif; Soylu, EmelBatteries are used to store electrical energy as chemical energy. They have a wide using area from portable equipment to electric vehicles. It is important to know the state of charge of a battery to use it efficiently. In this study, a graphical user interface is developed using a visual programming language to monitor the electrical situations of batteries. Cascade neural network, which is one of the most chosen artificial neural networks, is used to determine the type and state of charge of batteries. The software is able to identify type and state of charge of batteries online. Lead acid, Lithium Ion, Lithium polymer, Nickel Cadmium, Nickel Metal Hydride rechargeable batteries are used in experiments. The experimental results indicate that accurate estimation results can be obtained by the proposed method.
