A Conceptual Framework for Lithium-ion Battery RUL Prediction Using Deep Learning

Abstract

Abstract. In this paper, a conceptual framework for Remaining Useful Life (RUL) prediction of lithium-ion battery integrating deep learning is presented. The main processing stages, i.e., feature extraction, redundant information removal, data preprocessing, DNN model training, RUL prediction and evaluation, are discussed. Finally, a feature extraction method is presented by analyzing a lithium-ion battery data set from NASA AMES Center.