Browsing by Author "Demirci O."

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    Development of measurement and analyses system to estimate test results for lead-acid starter batteries
    (Elsevier Ltd, 2021) Demirci O.; Taskin S.
    The purpose of this paper is to provide a valid and applicable measurement and analysis system for performing test durations for Lead-Acid Started Batteries. To achieve this goal, two main objectives are followed. Firstly, a measurement system is designed to conduct all of the electrical tests, then an estimation algorithm is developed for automatic analyses and reporting proceedings. All measurement parameters are monitored in the developed user interface and saved in a database. For these operations, NI LabVIEW™ based user interface is developed. Based on the analyses of test results with different battery capacities, an automated evaluation report is obtained within the frame of EN-50342-1:2015 standard for each test. In this way, the reporting proceedings of test results are conducted through the given data acquisition devices and test analysis system. Test results are estimated with precision between 90% and 98% according to the test type indicated in the standard. Accordingly, the time is saved between 57% and 84% in test durations given in the standard. © 2020
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    Development of a control algorithm and conditioning monitoring for peak load balancing in smart grids with battery energy storage system
    (Turkiye Klinikleri, 2022) Atici T.; Taskin S.; Sengor I.; Tozak M.; Demirci O.
    As the traditional electricity grid transitions to the smart grid (SG), some emerging issues such as increased renewable energy penetration in the power system that cause load unbalances require new control methods. Storage of energy seems to be the best option to struggle with such issues. In this manner, energy storage technologies ensure the operating flexibility of the distribution system operator in the power system in terms of both sustainability of energy and peak load balancing. In this study, a grid condition monitoring user-interface and control algorithm is developed for the peak load reduction and supply-demand balancing in a SG system by using an energy storage unit. For this purpose, a battery energy storage system (BESS) is designed, scaled and integrated into the SG didactic test system, designed by the De Lorenzo Company. Online grid condition monitoring and control software is developed for grid-connected photovoltaic (PV) system and the BESS in the LabVIEW™ program. Moreover, an algorithm is developed that provides the conditions for the integration of the BESS into the system. The proposed algorithm is tested with real daily load data of the Manisa province in Turkey. Also, various case studies are performed to validate the effectiveness of the algorithm. Consequently, the proposed algorithm provides an average load factor improvement of 8.46% and the algorithm-controlled BESS increases the revenue of the system by 3.51% compared to the grid-connected PV system alone. © 2022 Turkiye Klinikleri. All rights reserved.
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    Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures
    (Elsevier Ltd, 2023) Acar Demirci B.; Demirci O.; Engin M.
    Epilepsy, a prevalent neurological disorder characterized by disrupted brain activity, affects over 70 million individuals worldwide, as reported by the World Health Organization (WHO). The development of computer-aided diagnosis systems has become vital in assessing epilepsy severity promptly and initiating timely treatment. These systems enable the detection of epileptic seizures by analyzing the electrical activity in the EEG recordings of the patients. In addition, it helps doctors to choose suitable treatment by quickly determining the type, duration, and characteristics of seizures and increases the patient's quality of life. The proposed computer-aided diagnosis system in this study comprises three modules: preprocessing, feature extraction, and classification. The initial module employs a low-pass Chebyshev II filter to eliminate noise artifacts from signal recordings. The second module involves deriving feature vectors using Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis. The third module employs the Artificial Neural Networks method for epileptic seizure detection. This study not only enables the comparison of feature extraction efficacy among Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis techniques, but it also reveals that Bispectrum Analysis and Empirical Mode Decomposition yield the highest accuracy rate. The method achieves 100% accuracy in detecting epileptic seizures. Additionally, sensitivity analysis has been conducted to enhance the success of Discrete Wavelet Transform and Wavelet Packet Analysis methods and to identify significant features. © 2023 Elsevier Ltd
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    Review of battery state estimation methods for electric vehicles-Part II: SOH estimation
    (Elsevier Ltd, 2024) Demirci O.; Taskin S.; Schaltz E.; Acar Demirci B.
    State of Health (SOH) significantly determines the performance and durability of EV batteries, with Battery Management System (BMS) playing a crucial role in enhancing their efficiency and operational cycle life. This comprehensive review, the second part of our series on Battery State Estimation Methods for Electric Vehicles, provides an in-depth exploration of SOH estimation methods. SOH, which encompasses a battery's overall health, capacity, and aging characteristics, plays a fundamental role in making informed decisions, conducting proactive maintenance, and ensuring the safe and reliable operation of EVs. Diverse SOH estimation methods, ranging from data-driven to model-based approaches, address the multifaceted challenges associated with battery aging, including electrochemical processes, temperature variations, usage patterns, and external factors. In recent years, data-driven methods, especially those rooted in machine learning and artificial intelligence, have gained prominence. These methods facilitate the discovery of complex models and correlations, encompassing battery degradation and using datasets to train algorithms. Machine learning algorithms including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Deep Learning (DL), have shown significant promise in estimating SOH by learning from historical data and adapting to varying operational conditions. The studies highlighted in this review demonstrate significant advancements in SOH estimation techniques, leading to improved accuracy, efficiency, and adaptability. These advances contribute to the development of more reliable BMSs for EVs and battery energy storage systems. © 2024 Elsevier Ltd
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    Review of battery state estimation methods for electric vehicles - Part I: SOC estimation
    (Elsevier Ltd, 2024) Demirci O.; Taskin S.; Schaltz E.; Acar Demirci B.
    This study presents a comprehensive review of State of Charge (SOC) estimation methods for Lithium-Ion (Li-Ion) batteries, with a specific focus on Electric Vehicles (EVs). The growing interest in EVs and the need for efficient battery management have driven advancements in SOC estimation techniques. Various approaches, including data-driven techniques, advanced filtering methods, and machine learning algorithms have been explored to enhance SOC estimation accuracy. The integration of artificial intelligence and hybrid models has shown promising results in improving SOC estimation performance. However, challenges remain in dealing with non-linear battery behavior, temperature variations, and diverse operating conditions. Researchers are continuously studying to improve the robustness and adaptability of SOC estimation methods to address these challenges. The primary objective of this study is to provide an up-to-date summary of the latest advancements in SOC estimation, offering insights into innovative approaches and developments in this field. All existing SOC methods, their advantages, challenges, and usage rates have been comprehensively examined with a specific focus on EV battery management systems. As the EV market continues to expand, accurate SOC estimation will remain essential for optimal battery management and overall EV performance. Future research will focus on refining existing algorithms, exploring new data-driven techniques, and integrating advanced sensor technologies to achieve real-time and reliable SOC estimation in EVs. © 2024 Elsevier Ltd