Lithium-ion battery remaining useful life prediction with Deep Belief Network and Relevance Vector Machine

Abstract

Lithium-ion batteries play critical roles in many electronic devices. It is necessary to develop a reliable and accurate remaining useful life (RUL) prediction approach to provide timely maintenance or replacement of battery systems. A fusion RUL prediction approach based on Deep Belief Network (DBN) and Relevance Vector Machine (RVM) is proposed in this paper. In the fusion prediction approach, DBN is responsible for extracting features from the capacity degradation of lithium-ion batteries, and RVM takes the extracted features as input to provide RUL prediction. The CALCE battery datasets are used to demonstrate the effectiveness of the proposed method. The results show that, compared with standard DBN and RVM, the proposed method has higher accuracy, more stable and reliable performance for lithium-ion batteries RUL prediction.