Prognostics and Health Management for Batteries: Comparative Analysis of Machine Learning Techniques for Accurate SoH Estimation

To improve battery performance and lifetime, this study highlights the value of prognostics and health management (PHM) in correctly predicting state of health (SoH). The Prognostics Center of Excellence Dataset Repository is used as a benchmark dataset in the study to compare and contrast different machine learning approaches for predicting SoH. The research compares how well different machine learning techniques, including Support Vector Regression, Extreme Gradient Boosting (XGBoost) and Random Forest, predict SoH. The study demonstrates the importance of PHM in improving battery reliability, efficiency, and provides insight into the effectiveness of different machine learning approaches in accurately predicting SoH.