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    Development of IoT Based Battery Management System
    (Ieee, 2021) Çeven, Süleyman; Küçükkülahli, Enver; Albayrak, Ahmet; Biçen, Yunus
    In this study, the modular battery management system used in electric vehicles was developed and monitored with the IoT-based MQTT protocol. In recent years, electric vehicles have been widely studied by researchers and long-lasting battery systems have been developed. In this study, a modular battery management system that performs the charging process with the passive balancing method has been developed. The battery has been developed for electric vehicles with more than 1 kWh of energy. There is temperature, current and voltage sensors at each node for battery management. A temperature sensor has been added to the system in case of overheating of the battery. The data collected over the battery is transferred to the web environment with MQTT, one of the IoT protocols. Passive balancing is preferred for balancing the batteries during charging. During charging, the MQTT server notifies the network to set the required voltage for each node.
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    Hardware-in-the-Loop Based State of Charge Estimation for Li-Ion Batteries using Multilayer Perceptron
    (2020) Çeven, Süleyman; Bayır, Raif
    In this study, hardware-in-the-loop based real-time state of charge estimation was performed in Li-Ion batteries, which are widely used in hybrid and battery electric vehicles. The state of charge is estimated on the Li-Ion battery cell that forms the electric vehicle battery system. Multi-layer perceptron approach has been preferred as a method for estimating the battery state of charge. Discharge experiments based on different electrical loads were applied to the Li-Ion battery cell to be used in multilayer perceptron learning processes. An experimental setup has been prepared to perform the discharge process under different electrical loads. In each discharge experiment, battery open circuit voltage, battery discharge current and battery cell temperature parameters were measured and were recorded. By using the data obtained from the experiments on the battery cell, a multilayer perceptron model was created in MATLAB environment. After creating the multilayer perceptron model, the real-time battery state of charge the was estimated at different discharge currents in the experimental setup and the results obtained were evaluated.
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    Real-time range estimation in electric vehicles using fuzzy logic classifier
    (Elsevier Ltd, 2020) Çeven, Süleyman; Albayrak, Ahmet; Bayır, 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. © 2020 Elsevier Ltd