Machine Learning Analysis of Lithium-Ion Battery Behavior and Prediction

Abstract

This paper analyzes lithium-ion battery datasets from NASA’s Prognostics Center, focusing on battery behavior and predictive modeling. Data preprocessing reveals distinct characteristics in voltage load and capacity distribution and insights into battery degradation and temperature profiles. The study also explores correlations between variables and utilizes a Random Forest algorithm to predict battery performance accurately, achieving low Root Mean Squared Error (RMSE) and high Coefficient of Determination ($\mathrm{R}^{2}$) values. This research provides valuable insights into battery behavior and optimization strategies for applications such as electric vehicles and renewable energy systems.