Elektrikli araçlarda kullanılan farklı kimyalara sahip lityum tabanlı pillerin sağlık durumlarının analizi ve modellenmesi
Küçük Resim Yok
Tarih
2025
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Yayıncı
İnönü Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Yapılmış olan Yüksek Lisans tez çalışmasında, elektrikli araçlarda kullanılan ve farklı kimyalara sahip lityum tabanlı pillerin sağlık durum analizi (SoH) ve modellenmesi yapılmıştır. Literatür çalışmaları sonucunda elektrikli araç teknolojilerinde yaygın olarak kullanılan pil kimyalarının şarj edilebilir pil kategorsinde olan lityum titanat oksit (LTO), lityum manganez oksit (LMO) ve lityum demir fosfat (LFP) olduğu gözlemlenmiştir. Bu doğrultuda tezin hazırlanmasında bu pil kimyalarına öncelik verilerek çalışma gerçekleştirilmiştir. Çalışma deneysel veri temini ve akabinde veri temelli yöntemlerin kullanılması ile gerçekleştirilmiştir. Belirlenen pil kimyalarının teknik özellikleri kapsamındaki maksimum şarj, minimum deşarj gerilim değerleri, çalışma akım aralığı, döngü ömrü ve sıcaklık gibi parametreler göz önünde bulundurularak döngüsel yaşlandırma işlemine tabi tutulmuştur. Elde edilen işlenmemiş veriler Python'da tez kapsamında hazırlanan bir yazılım ile ön işleme gerçekleştirilmiştir. Ön işleme sonrası elde edilen veriler Matlab programında Diferansiyel Voltaj Analizi (DVA) uygulanarak pillerin elektrokimyasal karakterstikleri hakkında çıkarımlar yapılmıştır ve yaşlanma karakteristikleri analiz edilmiştir. Akabinde, tüm piller için DVA sonucu elde edilen veriler Matlabde Regression Learner toolu kullanılarak regresyon analizine tabi tutulmuştur. Yapılan analiz sonrası oluşturulan modeller ile SoH tahmini yüksek doğrulukla gerçekleştirilmiştir. Çalışılan pil kimyalarının yaşlanma karakteristikleri ve sağlık durumlarının analizi sonucu elde edilen tüm bulgular kıyaslamalı olarak sunulmuştur. Çalışma daha önce beraber çalışılmamış üç farklı pil kimyasını ele almaktadır ve regresyon temelli makine öğrenimi yöntemleri ile pilin SoH analizi gerçekleştirmektedir. Bununla beraber, halihazırda yapılan SoH uygulamalarını farklı koşullara ve farklı kimyalara uygulayarak literatüre katkı sağlamayı amaçlamaktadır ve bu yönüyle yenilikçi, özgün bir çalışma olarak literatüre katkı sağlamaktadır. Hazırlanan Yüksek lisans tezi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP) Yüksek Lisans Tez projesi kapsamında FYL-2955 kodlu proje ile de desteklenmiştir.
In this master's thesis, the state-of-health (SoH) assessment and modeling of lithium-based batteries with different chemistries used in electric vehicles have been conducted. As a result of the literature review, it has been observed that lithium titanate oxide (LTO), lithium manganese oxide (LMO), and lithium iron phosphate (LFP) are commonly used battery chemistries in rechargeable battery categories within electric vehicle technologies. Accordingly, the study was carried out with a primary focus on these chemistries. The research was conducted by acquiring experimental data followed by the application of data- driven methods. The selected battery chemistries were subjected to cyclic aging processes based on their technical specifications, including parameters such as maximum charge and minimum discharge voltage limits, operational current ranges, cycle life, and temperature. The raw data obtained from the tests were preprocessed using custom software developed in Python within the scope of the thesis. Following the preprocessing stage, the data were analyzed in MATLAB using Differential Voltage Analysis (DVA) to extract insights into the electrochemical characteristics and aging behaviors of the batteries. Subsequently, the DVA results for all battery types were subjected to regression analysis using the Regression Learner tool in MATLAB. With the developed models, SoH estimation was successfully achieved with high accuracy. The findings regarding the aging characteristics and health conditions of the studied battery chemistries were presented in a comparative manner. This study addresses three different battery chemistries that have not been jointly investigated in prior research and performs SoH estimation using regression-based machine learning methods. Moreover, by applying existing SoH estimation techniques to different conditions and chemistries, the study aims to contribute to the literature in a novel and original way. This master's thesis was supported by the Scientific Research Projects Coordination Unit (BAP) under the project code FYL-2955, as part of a Master's Thesis Project.
In this master's thesis, the state-of-health (SoH) assessment and modeling of lithium-based batteries with different chemistries used in electric vehicles have been conducted. As a result of the literature review, it has been observed that lithium titanate oxide (LTO), lithium manganese oxide (LMO), and lithium iron phosphate (LFP) are commonly used battery chemistries in rechargeable battery categories within electric vehicle technologies. Accordingly, the study was carried out with a primary focus on these chemistries. The research was conducted by acquiring experimental data followed by the application of data- driven methods. The selected battery chemistries were subjected to cyclic aging processes based on their technical specifications, including parameters such as maximum charge and minimum discharge voltage limits, operational current ranges, cycle life, and temperature. The raw data obtained from the tests were preprocessed using custom software developed in Python within the scope of the thesis. Following the preprocessing stage, the data were analyzed in MATLAB using Differential Voltage Analysis (DVA) to extract insights into the electrochemical characteristics and aging behaviors of the batteries. Subsequently, the DVA results for all battery types were subjected to regression analysis using the Regression Learner tool in MATLAB. With the developed models, SoH estimation was successfully achieved with high accuracy. The findings regarding the aging characteristics and health conditions of the studied battery chemistries were presented in a comparative manner. This study addresses three different battery chemistries that have not been jointly investigated in prior research and performs SoH estimation using regression-based machine learning methods. Moreover, by applying existing SoH estimation techniques to different conditions and chemistries, the study aims to contribute to the literature in a novel and original way. This master's thesis was supported by the Scientific Research Projects Coordination Unit (BAP) under the project code FYL-2955, as part of a Master's Thesis Project.
Açıklama
Anahtar Kelimeler
Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
