Machine Learning-Based Approach for Predicting Battery Remaining Useful Life (RUL): A Data-Driven Methodology

In electric vehicles (EVs), lithium-ion batteries playa vital role; determining how long they will be useful is essential to ensuring their stability, longevity, and safety. In the subject of battery health management, it is crucial to provide a machine-learning approach for assessing battery health states that has high generalization, accuracy, and robustness. This work summarizes recent research on machine learning algorithms for data-driven approaches. The values of the variables, including current, voltage, and temperature, are used as the dataset. A Decision Tree (DT) algorithm and Gradient Boosting (GB) algorithm are proposed for the RUL prediction of batteries, and the model's error analysis is carried out to enhance a performance parameter for the battery. The prediction results are rated numerically using statistical markers like MAE, MSE, and RMSE. Also, the testing consequences revealed that the presented methods give better results than other machine learning techniques, considering various performance criteria.