## 2 Method of Research  
In this paper, in order to evaluate the performance of a representative artificial intelligence model used to estimate the remaining capacity of a lithium-ion battery, mean squared error, mean absolute error, and coefficient of determination (R-squared score) were used. They were used to evaluate the predictive accuracy of the model by measuring the difference between the model's predicted value and the actual value [7]. For the evaluation of the artificial intelligence model, the measurement data of the CS2 lithium-ion battery provided by the University of Maryland College of Engineering were downloaded and used as a dataset. The organization of the dataset was organized in the format shown in Figure 1. The data configuration included variables such as battery charge and discharge values, date and time, and in the data preprocessing stage, missing values were processed, outliers were removed, and variable conversion was performed to improve the quality of the data. In this study, five well-known artificial intelligence models: linear regression, decision tree, random forest, a neural network, and an ensemble model, were selected to find the most suitable model for estimating the remaining battery capacity from the experimental results. The linear regression model, the first artificial intelligence model selected, presented a method for estimating the state of health of a lithium-ion battery, and improved the predictive performance of the linear regression model through multimode degradation modeling and ensemble learning. This made it possible to analyze the charging and discharging behavior of batteries and to quantitatively assess and predict health status [8]. The basic structure of the linear regression model can be expressed in the following formula. \[\text{y\_pred}=W1X1+W2X2+W3X3+\ldots+WnXn+b \tag{1}\]  
In Equation (1) above, each element is an independent variable representing _XI_, _X2_, _X3_, \(\ldots\), _Xn_, representing an input variable (characteristic), and each characteristic value is entered into the model. _W1_, _W2_, _W3_, \(\ldots\), _Wn_ represent the weights of the input variables and control the importance of each input variable. b represents the bias and serves to adjust the y-intercept when the input variable is zero. y_pred is the value of the predicted output variable, computed according to a combination of input variables, weights, and biases. Accordingly, a linear regression model combines an input variable and the weights for that variable to form a linear equation for predicting an output variable, which allows modeling and prediction of a linear relationship between an input variable and an output variable. Second, as for previous studies on decision tree models, decision tree models were applied to analyze the charging and discharging processes of lithium-ion batteries and estimate battery capacity, and various decision tree models using data-based approaches were applied. Comparative analysis was performed and the optimal model was derived. Decision tree models learn decision rules for battery capacity prediction and derive prediction results based on input variables [9]. Third, similar to a previous study on the random forest model, the random forest model was used to predict the performance of a lithium-ion battery based on multi-mode degradation analysis, generating multiple decision trees and combining the prediction results of each tree. This improved the predictive performance. Through this, various performance indicators of the battery were predicted and the performance of the model was analyzed [10]. The basic structure of a random forest can be expressed as Equation (2), below [11]. \[ \begin{array}{l}\text{Ensemble\_Model}(X)=f_{1}(X)+f_{2}(X)+\ldots+f_{k}(X) \\ f_{i}(X)=\text{TreeModel}(X;\theta_{i})\\ \theta_{i}=\text{TrainTreeModel}(D_{i})\end{array}