Remaining useful life prediction of the lithium-ion battery based on CNN-LSTM fusion model and grey relational analysis

Abstract

The performance of lithium-ion batteries will decline dramatically with the increase in usage time, which will cause anxiety in using lithium-ion batteries. Some data-driven models have been employed to predict the remaining useful life (RUL) model of lithium-ion batteries. However, there are limitations to the accuracy and applicability of traditional machine learning models or just a single deep learning model. This paper presents a fusion model based on convolutional neural network (CNN) and long short-term memory network (LSTM), named CNN-LSTM, to measure the RUL of lithium-ion batteries. Firstly, this model uses the grey relational analysis to extract the main features affecting the RUL as the health index (HI) of the battery. In addition, the fusion model can capture the non-linear characteristics and time-space relationships well, which helps find the capacity decay and failure threshold of lithium-ion batteries. The experimental results show that: 1) Traditional machine learning is less effective than LSTM. 2) The CNN-LSTM fusion model is superior to the single LSTM model in predicting performance. 3) The proposed model is superior to other comparable models in error indexes, which could reach 0.36% and 0.38e-4 in mean absolute percentage error (MAPE) and mean square error (MSE), respectively. 4) The proposed model can accurately find the failure threshold and the decay fluctuation for the lithium-ion battery.

Keywords

• lithium-ion battery
• remaining useful life prediction
• convolutional neural network
• long short-term memory network
• fusion model