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Öğe CAN Communication Based Modular Type Battery Management System for Electric Vehicles(Kaunas Univ Technology, 2018) Turgut, Mustafa; Bayir, Raif; Duran, FecirLithium ion batteries are widely used in portable electronic devices and electric vehicles. Although battery technology has been significantly improved, it does not fully meet the energy requirements of electric vehicles. Electric vehicle batteries are built by serial and parallel connections of many cells to provide sufficient power. Differences between cells during battery usage can shorten the battery life and even worse can cause fire and explosion. Therefore, there is a need for a battery management system to ensure that the voltage, temperature and current information of the battery cells are used as optimum conditions. Cell balancing should be used to ensure that the battery cells are charging simultaneously. Cell voltage and temperature are measured by connecting auxiliary slave board to each series battery pack. This information is controlled by a master board. In this application, the excess energy is converted to heat by the load resistor on the utility boards. Fuzzy logic control is used to control the load resistor with the switching element. The charge status of the battery was estimated using the main battery current and the mains voltage with the master board. This application has been tested on an electric vehicle. A low cost modular battery management system has been developed that can control the safe charging and discharging of the vehicle battery.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 Determination of modulus of rupture and modulus of elasticity on flakeboard with fuzzy logic classifier(Elsevier Sci Ltd, 2009) Yapici, Fatih; Ozcifci, Ayhan; Akbulut, Turgay; Bayir, RaifIn this study, a model based on fuzzy logic classifier was created in order to determine the values of modulus of elasticity (MOE) and modulus of rupture (MOR) of flakeboards. MOR and MOE are the most important mechanical features of wood-composite panels. The most appropriate mixture ratios to be used in production of wood based boards were determined experimentally. These experiments are very expensive for the manufacturers and require time. For this purpose, MOE and MOR values were measured depending on flakes mixture ratios of manufactured boards. Using these values, input and output values and rule base of fuzzy logic classifier were created. With the fuzzy logic classifier model prepared in Matlab Simulink, MOR and MOE values for flakes mixture ratios were predicted. It was observed that the fuzzy logic classifier predicted MOR and MOE values with 95-97% accuracy. With this system, for the manufacture of wood-composite materials, the most appropriate chip mixture amount required by the manufacturer could be determined. (C) 2008 Elsevier Ltd. All rights reserved.Öğe The Determination of the Developments of Beehives via Artificial Neural Networks(Univ Osijek, Tech Fac, 2018) Bayir, Raif; Albayrak, AhmetHoneybees provide great benefits for people both with the foods they produce and as a pollinator. It is known that they pass the whole year with the foods they collect in spring and summer months. Beekeepers also benefit from the honey produced in these periods. Whether a beehive works adequately or not and its status of development can be understood through the observations by beekeepers. In this study, an Arduino-supported neural network model was developed in order to obtain information about the general situations of beehives. The three-input and three-output neural networks were embedded in a board after the training and testing stage. While temperature, humidity, and weight refer to inputs, good situation, stable situation, and bad situation represent outputs. The real-time model has an accuracy of 99.84%.Öğe Development and evaluation of a web-based intelligent decision support system for migratory beekeepers in Turkey to follow nectar resources(Taylor & Francis Ltd, 2021) Albayrak, Ahmet; Duran, Fecir; Bayir, RaifIn honey production, high yields can be achieved with migratory beekeeping. Migratory beekeepers complete the honey production season by moving their colonies to areas with high nectar flow. Traditionally migratory beekeepers decide where to go based on their previous experience. Nowadays, given the rapidly changing climatic conditions, it is seen that the regions to be visited should be decided with more qualified information rather than experience. In this study, a web-based information system was developed which provides qualified information about the regions to be visited by migrant beekeepers. The information system takes into account the nectar flow and climatic conditions in the regions to be visited. These two important factors were evaluated using fuzzy cognitive maps (FCM) and an intelligent decision support system (IDSS) was developed. The cognitive map was analysed statistically in the conceptual modelling stage of FCM and it was found that it explained 82.3% of beekeeping potential according to multiple linear regression. Using IDSS, migratory beekeepers can learn the honey yield (good, average, and bad) they can obtain from the regions they will visit. The information system was also compared with the measurement results made with the wireless sensor network (WSN) and the migratory beekeeper information for 2017. As a result, it was found that IDSS operates with 79.8% accuracy.Öğe Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor(Korean Inst Intelligent Systems, 2020) Matli, Musa; Albayrak, Ahmet; Bayir, RaifThis study aims to present an idea about the practical consequences of using mobile robots with Mecanum wheels. For mobile robots, an approach is proposed to avoid obstacles without location and map information. This approach is presented using a series of developed solutions. This article shares the process on how a set of discussed conceptual methodologies can be applied as well as their practical results. This method is provided using fuzzy logic and gap tracking. LIDAR is used to recognize obstacles around the mobile robot. By using the LIDAR, the robot detects gaps around it and moves according to fuzzy logic. The fuzzy logic consists of three inputs, an output, and 45 rules. The first of the membership functions represents the membership function that replaces the obstacle. The second membership function calculates the distance to the obstacle. The final login membership function is used to determine the angle between the obstacle and robot view. The output membership function represents the membership function that moves the robot. The results are analyzed under three different scenarios with five different experiments for each scenario. The results show that the mobile robot can avoid obstacles without location and map information. We believe that the proposed method can be used in mobile robots such as guard and service robots.Öğe Development of Information System for Efficient Use of Nectar Resources and Increase Honey Yield per Colony(Univ Putra Malaysia Press, 2019) Albayrak, Ahmet; Bayir, RaifIn this study, for 5.1 million bee colonies and nearly 42 thousand migratory beekeepers in Turkey, an information system is recommended that determines the areas where the honey season will pass taking into account the flowering periods of plants. Migratory beekeepers produce honey by following the flowering periods of nectar sources. Bee colonies should be placed in the optimum number in areas with nectar sources. Less colony settlement has a negative impact on agricultural production. Colony condensation also adversely affects the honey yield of bee colonies per hive. In this study focuses on the optimal number of colonies in the nectar region. In the first stage, 81 provinces in Turkey were analyzed in terms of nectar resources and meteorological conditions which are the major sources of honey production. This evaluation used fuzzy cognitive maps. As a result of the evaluation, 33 provinces were identified as the most suitable provinces in terms of nectar sources and meteorological conditions. In the second phase of the study, a new approach has been proposed for migratory beekeepers to pass the nectar flow season at maximum efficiency and to use nectar resources at maximum level. This approach is based on the placement of bee colonies, considering the potential of the bee farming of the regions and the number of bee colonies subjected to migratory beekeeping. One of the advantages of this approach is that it will maximize honey yield per colony for migratory beekeepers. Another advantage of this system is that the distribution of bee colonies according to the number of plants in the region will be positive in terms of quality and quantity of agricultural production.Öğe Development of intelligent decision support system using fuzzy cognitive maps for migratory beekeepers(Tubitak Scientific & Technological Research Council Turkey, 2018) Albayrak, Ahmet; Duran, Fecir; Bayir, RaifThis study presents the development of an intelligent information system using fuzzy cognitive maps that provides information to migratory beekeepers about the nectar flow and climate conditions in the regions they will visit. Beekeeping is an agricultural activity essentially focused on honey production. High honey yields in beekeeping can be achieved through migratory beekeeping. Migratory beekeepers complete the honey production season by carrying their hives to regions with high nectar flow. Beekeepers decide on the regions they will visit based on their previous experiences. In this study, a software-based system that provides information to the beekeepers about the honey yield in the regions they will visit has been developed. It is an intelligent information system developed using fuzzy cognitive maps that helps the beekeepers in choosing the region they will visit.Öğe Downhill Speed Control of In-Wheel Motor During Regenerative Braking(Kaunas Univ Technology, 2017) Bayir, Raif; Soylu, TuncayRecovering kinetic energy in an electric vehicle is important in order to use battery more effective and to extend the vehicle's maximum distance range. In this study, regenerative braking is simulated and implemented on a test bed for a lightweight electric vehicle on the three different downhill that are 3 degrees, 4 degrees and 5 degrees slopes at 30km/h conditions. An in-wheel motor which is generally used in the light electric vehicle is used for the regenerative braking application. Speed of the in-wheel motor is controlled with PID controller during regenerative braking. The in-wheel motor charges batteries and the PID controller maintain speed of the in-wheel motor to reference speed in both simulation study and experimental study. Graphics and outcomes of the simulation and experiment are showed in the results and discussion section.Öğe Drive Mode Estimation for Electric Vehicles via Fuzzy Logic(Ieee, 2018) Duran, Fecir; Ceven, Suleyman; Bayir, RaifElectric vehicles are widely used today. The biggest problem of electric vehicles are limited energy and limited range. Using energy efficiently is an important issue. In this study, we monitored an electric vehicle's systems and power consuming systems in real time. We predicted the optimal driving mode with electronic control unit using Fuzzy Logic Control. We tested system on electric golf car real time. The measurement parameters are transferred to Vehicle Control Unit via Controller Area Network. The driver can choose driving mode as economical driving, normal driving and sporty driving. Experimental results indicate that our Electronic Control Unit increases the driving range of the electric vehicle.Öğe Enhancing Road Safety: Real-Time Distracted Driver Detection Using Nvidia Jetson Nano and YOLOv8(Ieee, 2024) Neamah, Osamah N.; Almohamad, Tarik Adnan; Bayir, RaifThis study introduces an innovative approach that combines cutting-edge technology and advanced models for real-time applications. Leveraging the performance of the Nvidia Jetson Nano, alongside an integrated camera and GSM/GPS module, our innovative system demonstrates both its practicality and versatility. Specifically, by employing the YOLOv8 classification model for handling State Farm Distracted Driver Detection data which underscores its adaptability and effectiveness in this critical domain. Additionally, our research thoroughly assesses computational efficiency, exploring both hardware and software-based analysis methods. This work is a cornerstone in harnessing technology for real-world impact, merging innovation with practicality and comprehensive evaluation.Öğe A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm(Gazi Univ, 2011) Bay, Omer Faruk; Bayir, RaifUncertainties in the system models, the presence of noise and the stochastic behavior of several variables reduce the reliability and robustness of the fault diagnosis methods. For overcoming these kinds of problems, this study proposes the fault diagnosis of starter motors based on fuzzy logic methodology. A starter motor is a serial wound dc motor which is used for running the Internal Combustion Engine (ICE). If a fault occurs with the starter motor, the ICE cannot be run. Especially in emergency vehicles (such as ambulance, fire engine, etc), starter motor faults causes any other faults. In this study, a fuzzy logic based fault detection system has been developed for implementation on emergency vehicles. Information of the current and the voltage of a starter motor is acquired and then practiced on a fuzzy logic fault diagnosis system (FLFDS). For this purpose, a graphical user interface (GUI) software is developed by using Visual Basic 6.0 programming language. FLFDS is effective in detection of six types of starter motor faults. The proposed system can be used in a Quality Control unit of manufacturers and maintenance-repairing units.Öğe Implementation of collaborative multi-robot system carrying cargos autonomously(2021) Budak, Emrah; Duran, Fecir; Yatak, Meral Özarslan; Bayir, RaifThe paper presents implementation of collaborative multi-robot system for carrying cargo autonomously. Multi-robot systems are especially used to carry cargos to target place in the shortest way in the shortest duration by path planning. This system is composed of two robots called as Leader and Assistant. They sense the cargo with load cells on themselves and carry it to the target place. After determination of the cargo, if its weight is in the limits of the weights for Leader, it pushes the cargo by itself and Assistant waits on standby mode. If the cargo is higher than carrying capacity of Leader, Assistant is called and both push it to the target. Detecting cargos task is performed with a method similar to method of calculating fitness value. Carrying cargos task was performed by finding the shortest way with curve fitting algorithm. Carrying cargos with multi-robots by using curve fitting is the most practical solution. Consequently, reducing the route by 13.7% could be provided successfully by this algorithm instead of line following method and so energy saving was ensured. Task performance rate for carrying the cargo to the target place is achieved up to 90% for stand-alone and cooperative operation.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 Modeling of migratory beekeeper behaviors with machine learning approach using meteorological and environmental variables: The case of Turkey(Elsevier, 2021) Albayrak, Ahmet; Ceven, Suleyman; Bayir, RaifIn this study, migratory beekeeping behavior, which is an important form of beekeeping, has been modeled. Modeling was performed in conditions of Turkey. Modeling was made by considering food sources (nectar / pollen) and meteorological variables (temperature, humidity, number of rainy days, number of cloudy days and sunshine duration) for Turkey in which migratory beekeeping carried out in a different form than in developed countries. The main output in migratory beekeeping is honey production. Considering honey production, modeling has been made with the food sources and meteorological variables that have the greatest effect on honey production. Since the data set developed for modeling consists of relatively few samples, the ensemble learning approach was preferred from the machine learning approaches. Random Forest and Decision Tree algorithms, which are among the ensemble learning techniques, were used. As a result, the migratory beekeeping behavior was correctly classified at a rate of 92%. As a result of classification of Turkey's 81 provinces in five different categories, it was concluded that 33 provinces are suitable for migratory beekeeping at different times of the year. These 33 provinces are regions in the good and very good categories. In the next stage, thematic maps were produced for migratory beekeepers. Maps were produced for each month of the year. Thus, a guidance and information system has been obtained for migratory beekeepers.Öğe The monitoring of nectar flow period of honey bees using wireless sensor networks(Sage Publications Inc, 2016) Bayir, Raif; Albayrak, AhmetHoney bees are extremely important creatures to humans that are able to pollinate flowers and produce products such as honey, bee pollen, and royal jelly. Honey is the primary product of beekeeping, and the practice of migratory beekeeping is the most profitable beekeeping method carried out by monitoring the nectar sources. Monitoring these nectar sources provides knowledge about the bloom periods of plant species. Using a wireless sensor network, this study aims to monitor the nectar flow in the region that the migratory beekeepers plan to visit. The wireless sensor network developed was tested in a region with a known nectar flow period from June to July. During the test period, the hive weight and ambient temperature and humidity were constantly measured. The real-time data were available via a website. The nectar flow is determined based on the changes in hive weight and these measurements provide the beekeepers with information about the instantaneous, hourly, and daily nectar flow in the specified region.Öğe Project and group based learning and competition based evaluation in lesson of microcontroller applications(Elsevier Science Bv, 2009) Kocak, Emel; Bayir, RaifA process control which requires software and hardware has been given to students as a project study in microcontroller education. The class divided into groups of 2 student each and given names to each group. A temperature control which is the mostly used application of microcontrollers in industry has been chosen as the application. Various stages have been determined for facilitating to realize the project. Selections of equipments that will be used in application and writing of codes are performed by each group after the technical information given by the instructor. Students have behaved freely on their selections. On their selections by considering advantages and disadvantages they have endeavor to ensure supremacy for winning the race. Basic subprograms are presented to groups for controlling of the project with microcontroller and writing of code. Students may change these codes or add new codes. By running the projects in the same ambient the most correct running one is chosen as the first. The penalty points have been calculated considering constraints and rules. The first project is evaluated with the highest score. Projects of other groups are sorted by considering faults and running times of the projects. Scores of these groups are calculated by multiplying the standard deviation and a coefficient. Realization of the intended phases on time is added the evaluation. With group and project based working, the students have learned controlling of a process by using microcontroller, choosing convenient equipments to controller and realization of essential code. By means of this project, students have gained information and abilities necessary for industrial needs. Evaluation of the projects by a racing urges the student to a competition and increases success. Thanks to this microcontroller education, our students gain degree at national and international competitions based on microcontroller in various areas. (C) 2009 Elsevier Ltd. All rights reservedÖğe 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.Öğe Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network(Zhejiang Univ, 2013) Uysal, Ali; Bayir, RaifThe faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.Öğe REAL-TIME FAULT DIAGNOSIS OF HUB MOTORS USING A FUZZY-LOGIC CONTROLLER(St John Patrick Publ, 2016) Simsir, Mehmet; Bayir, Raif; Uyaroglu, Yilmaz[No abstract available]
