Advancing Lithium-ion Battery Prognostics: A Novel Deep Learning Framework for Enhanced SOH and RUL Prediction Accuracy

Abstract

The field of Lithium-ion battery prognostics has witnessed a surge in research employing deep learning methodologies to forecast both the Remaining Useful Life (RUL) and the State of Health (SOH) of these crucial energy storage units. Given the paramount importance of accurately monitoring and predicting SOH and RUL in ensuring the reliability and longevity of battery systems across diverse applications, our study introduces an innovative approach poised to set a new benchmark in this domain. Our research represents a significant contribution to the existing literature by presenting an enhanced method that surpasses the predictive capabilities of previously published works. Central to our investigation is the development of a hybrid deep learning framework, leveraging a combination of Convolutional Neural Network (CNN), Deep Neural Networks (DNN), and Gated Recurrent Units (GRU). This novel approach was meticulously designed and rigorously tested, resulting in unparalleled levels of prediction accuracy. To assess performance, we employed comprehensive evaluation metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and absolute error (AE) on NASA and CALCE datasets cycle life data. Through meticulous experimentation and analysis, our study sets a new standard for RUL and SOH prediction accuracy in Lithium-ion battery management, offering valuable insights for advancing the field and enhancing the reliability of battery systems across various applications.