[논문]Analysis of Characteristics of All Solid-State Batteries Using Linear Regression Models

Kyo-Chan Lee; Sang-Hyun Lee

doi:10.7236/ijasc.2024.13.1.206

[국내논문] Analysis of Characteristics of All Solid-State Batteries Using Linear Regression Models

International journal of advanced smart convergence, v.13 no.1, 2024년, pp.206 - 211

Kyo-Chan Lee (TMC Co., Ltd) , Sang-Hyun Lee (Department of Computer Engineering, Honam University)

Abstract ▼ AI-Helper

This study used a total of 205,565 datasets of 'voltage', 'current', '℃', and 'time(s)' to systematically analyze the properties and performance of solid electrolytes. As a method for characterizing solid electrolytes, a linear regression model, one of the machine learning models, is used to visualize the relationship between 'voltage' and 'current' and calculate the regression coefficient, mean squared error (MSE), and coefficient of determination (R^2). The regression coefficient between 'Voltage' and 'Current' in the results of the linear regression model is about 1.89, indicating that 'Voltage' has a positive effect on 'Current', and it is expected that the current will increase by about 1.89 times as the voltage increases. MSE found that the mean squared error between the model's predicted and actual values was about 0.3, with smaller values closer to the model's predictions to the actual values. The coefficient of determination (R^2) is about 0.25, which can be interpreted as explaining 25% of the data.

주제어

표/그림 (7)

그림 Figure 1. Processes for Solid-State Battery Analysis
그림 Figure 2. Pseudocode for analyzing the results of linear regression models
그림 Figure 3. Output summary statistics of data and visualize relationships between variables
표 Table 1. Descriptive statistics of datasets
그림 Figure 4. Correlation between Voltage, Temperature (°C), and Time
표 Table 2. Results of Regression Analysis
그림 Figure 5. Results of Regression Analysis graph

참고문헌 (6)

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