State of health analysis and modelling of lithium - based batteries with different chemistry used in electric vehicles

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.