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    CAN Communication Based Modular Type Battery Management System for Electric Vehicles
    (Kaunas Univ Technology, 2018) Turgut, Mustafa; Bayir, Raif; Duran, Fecir
    Lithium 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.
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    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, Raif
    In 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.
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    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, Raif
    This 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.
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    Development of intelligent decision support system using fuzzy cognitive maps for migratory beekeepers
    (2018) Albayrak, Ahmet; Bayır, Raif; Duran, Fecir
    This study presents the development of an intelligent information system using fuzzy cognitive maps thatprovides 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 canbe achieved through migratory beekeeping. Migratory beekeepers complete the honey production season by carryingtheir hives to regions with high nectar flow. Beekeepers decide on the regions they will visit based on their previousexperiences. In this study, a software-based system that provides information to the beekeepers about the honey yieldin the regions they will visit has been developed. It is an intelligent information system developed using fuzzy cognitivemaps that helps the beekeepers in choosing the region they will visit
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    Drive Mode Estimation for Electric Vehicles via Fuzzy Logic
    (Ieee, 2018) Duran, Fecir; Ceven, Suleyman; Bayir, Raif
    Electric 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.
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    Implementation of collaborative multi-robot system carrying cargos autonomously
    (2021) Budak, Emrah; Duran, Fecir; Yatak, Meral Özarslan; Bayir, Raif
    The 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.
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    Real-time trajectory tracking of an unmanned aerial vehicle using a self-tuning fuzzy proportional integral derivative controller
    (Sage Publications Ltd, 2016) Demir, Batikan E.; Bayir, Raif; Duran, Fecir
    In the present study, a desired reference trajectory was autonomously tracked by means of a quadrotor unmanned aerial vehicle with a self-tuning fuzzy proportional integral derivative controller. A proportional integral derivative controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize the quadrotor, to control the attitude and to track the trajectory. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of unmanned aerial vehicle and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which produce pitch, roll, yaw and height values. The fuzzy proportional integral derivative control algorithm is real-time applied to the quadrotor in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and self-tuning fuzzy proportional integral derivative controller have accomplished to track a defined trajectory with the aircraft, the self-tuning fuzzy proportional integral derivative controller has been able to control with less errors than the classical proportional integral derivative controller.