Random Forest-Based Machine Learning Model Design for 21,700/5 Ah Lithium Cell Health Prediction Using Experimental Data

In this research, the use of machine learning techniques for predicting the state of health (SoH) of 5 Ah—21,700 lithium-ion cells were explored; data from an experimental aging test were used to build the prediction model. The main objective of this work is to develop a robust model for battery health estimation, which is crucial for enhancing the lifespan and performance of lithium-ion batteries in different applications, such as electric vehicles and energy storage systems. Two machine learning models: support vector regression (SVR) and random forest (RF) were designed and evaluated. The random forest model, which is a novel strategy for SoH prediction application, was trained using experimental features, including current (A), potential (V), and temperature (°C), and tuned through a grid search for performance optimization. The developed models were evaluated using two performance metrics, including R2 and root mean squared error (RMSE). The obtained results show that the random forest model outperformed the SVR model, achieving an R2 of 0.92 and an RMSE of 0.06, compared to an R2 of 0.85 and an RMSE of 0.08 for SVR. These findings demonstrate that random forest is an effective and robust strategy for SoH prediction, offering a promising alternative to existing SoH monitoring strategies.