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.

I. Introduction

The short life of batteries makes their use in energy systems one of the biggest problems. Batteries deteriorate over time, which can lead to reduced capacity and performance. As a result, the battery may fail prematurely, which can be expensive and inconvenient. For effective energy management and sustainability, it is essential to accurately estimate the Remaining Useful Life (RUL), State of Charge (SoC) and State of Health (SoH) of batteries. Accurately predicting the RUL, SoC and SoH of batteries is a difficult task that requires specialised techniques and equipment. It involves tracking the battery's performance over time and using this information to estimate how long the battery will last. This information can be used to optimize energy management strategies and ensure that the battery is replaced before it fails.