## 2 Materials and Methods
### Machine Learning Models Used for SoH Prediction  
Various machine learning (ML) methods have been used for SoH prediction. LR is one of the different ML methods. Using LIB data, LR creates a linear model that predicts the SoH of the battery. For example, if there is a linear relationship between the SoH of the battery and the operating temperature, the model attempts to find this relationship and predicts the SoH of the battery. During the training process, the model creates a linear equation to express this relationship using measurements in the dataset. The learned model represents the linear relationship between data points. This model is then used to predict new data points. SVM, which is frequently used in SoH estimation, separates data points into groups by creating a separation limiter. The DT and RF target specific functions to determine the SoH of the battery by processing complex data structures. It classifies data in a tree structure \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Discharge** & & **Ambient Temperature (\({}^{\circ}\)C)** & & **Charge Cut-Off** \\
**C-Rate** & **10** & **25** & **45** & **60** & **C-Rate** \\ \hline \multirow{3}{*}{0.7C} & Test No. 1 & Test No. 7 & Test No. 13 & Test No. 19 & C/5 \\  & Test No. 2 & Test No. 8 & Test No. 14 & Test No. 20 & C/40 \\ \cline{1-1}  & Test No. 3 & Test No. 9 & Test No. 15 & Test No. 21 & C/5 \\ \cline{1-1}  & Test No. 4 & Test No. 10 & Test No. 16 & Test No. 22 & C/40 \\ \cline{1-1}  & Test No. 5 & Test No. 11 & Test No. 17 & Test No. 23 & C/5 \\ \cline{1-1}  & Test No. 6 & Test No. 12 & Test No. 18 & Test No. 24 & C/40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test parameters applied to cells [21]. and makes predictions. Clustering algorithms divide the data into groups and identify battery groups with similar features. It parses and groups data with similar characteristics. Clustering algorithms often use different metrics to measure similarities in the dataset. To illustrate, metrics such as Euclidean distance and Manhattan distance can be used to calculate the distance between data points. Data points are divided into groups based on these metrics, and each group contains data points that are close to each other in terms of similarity. Clustering algorithms can be a useful tool for understanding the behavior of batteries and identifying batteries with similar characteristics. However, rather than directly predicting battery health when used alone, it reveals the relationship between batteries with similar characteristics and provides a basis for further analysis. Advanced ANNs predict the SoH of the battery by analyzing complex relationships in the data. This method mimics the way the human brain works. The ANN is used to identify and learn complex relationships by analyzing large amounts of data. When battery data are given as input to the ANN, neurons within the network perform mathematical operations on these data. The ANN uses mechanisms such as weighting and activation functions to identify and learn patterns in the dataset. In this way, it can identify complex relationships in battery data and use these learnings to predict important features such as the SoH. One of the frequently used methods in SoH prediction processes using ML is the regression model. Regression is used as a model in the relationship between two or more independent variables and a dependent variable. The main purpose of this modeling is to predict the dependent variable through the use of independent variables. In this study, models such as LR and robust regression were implemented. DT is an algorithm similar to a tree structure used in regression problems. The uppermost part consists of roots and branches; there are leaves at the end of the branches. It is generally preferred with complex datasets and works well with datasets that do not require preprocessing. To utilize decision trees, different models, such as the fine tree, medium tree, and coarse tree, have been used to compare the prediction performance.