Batteries by Algorithm: Unleashing the Potential of Machine Learning in RUL Prediction

For a battery to operate effectively and reliably, a battery management system (BMS) is essential. BMS remaining useful life (RUL) is predicted using machine learning. In order to accomplish this, this article investigates numerous machine learning techniques, including logistic regression (LR), naive Bayes (NB), decision trees, and support vector machines (SVM). For model training and testing, a sizable dataset comprising important variables is used, and data visualisation techniques boost prediction precision. This study shows how well machine learning performs BMS RUL estimation, potentially improving battery maintenance and system performance.