## 3 Experimental Studies  
In the present study, an estimation of the discharge capacities of LIBs was attempted by developing different models. The first model was the LR model, based on machine learning. The other models were the following: DT, SVM, GPR, kernel SVM, kernel LSR, boosted tree, bagged tree, neural network, and hybrid structures of these models based on LSTM. The development of these models should be carried out in a certain order. The data collection and preprocessing steps have been the first and most important steps in model development. You need to collect historical data on the problem you want to model (discharge capacity estimation), preprocess this data, and have it checked by real users for data consistency. In this study, the first 300 cycles in the dataset were taken. This selection was checked by real users, and outliers and missing data were checked. In the second step, selecting and analyzing important features from the existing data was \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Advantage** & **Disadvantage** \\ \hline Linear Regression & Simple to apply, easy to interpret. & It is limited to linearity, not suitable for use if there is no relationship between variables, and is sensitive to noise. \\ \hline Decision Tree & It is robust to independent variables, adaptable to nonlinear data, has high prediction success, can deal with categorized values, and does not need too much data. & Variable to data change, unstable with noise, overfitting, not smooth or continuous, unsuitable for unstable data classes, computationally costly for large data. \\ \hline Support Vector Machine & Works well when separation is clear, becomes more effective as size increases, and is memory efficient. & It is not suitable for large datasets; noise negatively affects the results. \\ \hline Efficient Linear & It is easy to apply and understand. & As the number of variables increases, adaptability decreases. \\ \hline Gaussian Process Regression & Provides reliable intervals; suitable for uncertainty estimation. & It is only for data with positive values; not suitable for data with noise. \\ \hline Kernel & It can capture complex and nonlinear patterns, save memory, and provide flexibility. & Interpretation and explanation of the model are difficult. As feature size increases, extension becomes difficult and overfits new data. \\ \hline Ensemble & Good for complex and noisy problems in decision trees, adjusts variance adjustment. & For multiple models, computational costs are high, and interpretation and explanation are difficult. \\ \hline Neural Network & It provides effective visual capability, the ability to process data even if it is not edited, and it is adaptable and offers a user interface. & It has high hardware requirements, data-based operation, poor control capability, and may produce incomplete results. \\ \hline Long Short-Term Memory & It can capture long dependencies and handles sequential data quite well. & Hardware costs are high; the dataset must be large. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and disadvantages of ML and ANN models. carried out. The selected features helped to optimally determine the value of the target variable (the variable you are trying to predict in regression problems). In the third step, it is necessary to divide the dataset into training and test datasets. The training set was used to train the model; on the other hand, the test set was used to evaluate the performance of the model. The model's development and training phase varies depending on the basic features of the ML or ANN algorithms that have been used. Hence, it has been provided in detail under different headings in the Materials and Methods section. The performance of the developed model has been evaluated using a test dataset. Performance evaluation assists in determining how accurately the model predicts the target variable. RMSE is a commonly used performance measure for regression problems.