Yazar "Ceven, Suleyman" seçeneğine göre listele

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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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    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, Raif
    In 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.
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    Real-time range estimation in electric vehicles using fuzzy logic classifier
    (Pergamon-Elsevier Science Ltd, 2020) Ceven, Suleyman; Albayrak, Ahmet; Bayir, Raif
    Nowadays, many scientists and companies in the automotive sector in the world are undertaking many important studies on electric vehicle technologies. For the electric vehicle to function as desired, the subsystems of the vehicle must be monitored and the parameters related to the vehicle must be kept in the most efficient range. Efficient use of these systems in electric vehicle will increase the vehicle range, as well as ensure the long life of the components used in the vehicle subsystems. Today, problem areas such as calculating the range of electric vehicles and battery state of charge have not yet been sufficiently standardized. The aim of this study is to make a range estimation in electric vehicle with fuzzy logic classifier which has been successfully applied in various problem areas. The fuzzy logic classifier is designed for range estimation, which is one of the most important research areas of electric vehicles today. In the Mamdani type fuzzy logic approach, dynamic vehicle parameters are taken into consideration. The fuzzy logic classifier considers the battery parameters of the vehicle and the power consumed instantly. In the prediction system, the power spent on the vehicle and the battery charge status are selected as inputs. The developed system was evaluated with three different test scenarios on the same track. These tests were conducted with no load (driver only), half load (driver + one person) and fully load (driver + three persons). The fuzzy logic classifier system determines in real-time how far electric vehicle can travel. (C) 2020 Elsevier Ltd. All rights reserved.