Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Empirical Mode Decomposition and Deep Neural Networks

Abstract

The prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is vital for the battery management system used in electric vehicles. We can avoid unnecessary losses if we can accurately predict the RUL of batteries and replace batteries on time. This study proposes a method for predicting the RUL of LIBs based on empirical mode decomposition, deep neural network (DNN), and the long short-term memory model. We then extract the discharge data of LIBs. Subsequently, by applying empirical mode decomposition, the dischargeable capacity of the LIBs is decomposed into a global deterioration trend and capacity regeneration. The long short-term memory model is then applied to predict capacity regeneration, while DNNs predict global deterioration trend. Finally, we add the individual predicted results to obtain the dischargeable capacity of the LIBs; consequently, we obtain the RUL of the LIBs. The proposed method yields a more accurate prediction result than the mixed model of empirical mode decomposition and autoregressive integrated moving average model.

Keywords

• Deep neural network
• empirical mode decomposition
• lithium-ion batteries
• remaining useful life