Degradation Prognostics of Lithium-ion Batteries Based on Partial Features and Long Short-term Memory Network

Abstract

The accurate degradation prediction of Lithium-ion batteries is beneficial to the reliability and safety of battery-driven systems. In this paper, a long short-term memory network (LSTM) model is utilized to predict the capacity degradation trend using partial charge and discharge features of Lithium-ion batteries. Firstly, significant features are extracted from the original charge and discharge data. Then the Pearson correlation coefficient is adopted to filter the features with high correlation coefficients. Selected features are subsequently treated as the input of the prediction model. Finally, a LSTM model is developed and associated hyperparameters are established by Adam algorithm. The proposed method is validated by experimental results on the NASA battery dataset.