## 1 Introduction  
Lithium-ion batteries have been most used since the 1990s due to their high energy density, large capacity, ability to achieve high efficiency, easy generation of high power and light weight [1; 2]. On the other hand, NASA recognized the importance of defect prediction after an accident in 2000 prevented a spacecraft from achieving the Mars exploration goal, and recognized the need for defect prediction and safety management using big data and machine learning. If the remaining life of a system or equipment can be predicted, accidents or failures in areas that are difficult for workers to access can be prevented in advance, and operation disruptions due to failures can be responded to more quickly. This can reduce not only maintenance costs, but also the opportunity costs incurred by making products difficult to produce due to downtime during repairs [3]. Recently, various models using artificial neural network theory that learn and provide answers to problems are being used in the industrial field for such predictions [4].  
The field of artificial intelligence is constantly evolving, and new models and algorithms are constantly being developed and improved. Performance comparison analysis can contribute to the research and development of artificial intelligence models, the exploration of new technologies and approaches, and can improve our understanding of battery performance prediction. Estimating the remaining capacity of a lithium-ion battery can be very useful for battery life and performance management, charge and discharge control, and energy management system optimization. Various artificial intelligence models have been developed that have strong predictive capabilities in estimating remaining capacity.  
Therefore, we want to compare and analyze the performance of these AI models to find out which model has the best predictive performance. This allows you to select the best model for estimating the remaining capacity of real batteries and improve the efficiency of battery management and energy systems [5].  
Therefore, in this paper, a linear regression model, a decision tree model, a random forest model, and a neural network model are used as representative artificial intelligence models that can be used to estimate the remaining capacity of a lithium-ion battery. We also utilize measurement data from CS2 lithium-ion batteries measured at the University of Maryland College of Engineering to evaluate the performance of the ensemble model [6]. We want to evaluate the predictive accuracy of the model using the model's mean square error, mean absolute error, coefficient of determination, and root mean square error as comparative parameters. The predictive accuracy of a model needs to be evaluated by measuring the difference between the model's predicted value and the actual value.  
The purpose of this study is to predict the remaining capacity of a lithium-ion battery and improve its performance through three machine learning models, such as linear regression, decision tree, and random forest, which are existing artificial intelligence models, and two deep learning models, such as neural network and deep learning, in order to evaluate and contribute to future basic research. Here, the prediction accuracy was measured using the MSE, MAE, coefficient of determination (R-square), and RMSE to evaluate the performance of each AI model. In addition, the measurement data of the CS2 lithium-ion battery provided by the University of Maryland College of Engineering were used to evaluate the artificial intelligence model.  
This paper is organized as follows: describes the composition and research method of the dataset to train the artificial intelligence model proposed in this study; Section 3 describes the artificial intelligence model used in this paper; Section 4 describes the experimental results; and finally, Section 5 discusses the conclusion of this paper.