Lithium battery life prediction based on deep learning

Abstract

Li-ion batteries have the advantages of high efficiency, high energy density and long life, having developed rapidly in recent years. However, it is tough to accurately predict the decline trend of Li-ion battery capacity, which limits the further improvement of their service life and safety. In this study, a method of using wavelet denoising to preprocess the data and predicting the life of Li-ion battery based on whale optimization algorithm combined with long short-term memory network (WOA-LSTM) is proposed. In this paper, two sets of data sets B0005 and B0006 of NASA 's public data set are used. The original battery capacity data is subjected to wavelet transform and noise reduction to remove noise and redundant information. The calculation results of SNR and RMSE are 48.1119 and 0.006225, respectively. Then LSTM (Long short-term memory), RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit) and WOA-LSTM are used to predict the remaining useful life of the data set RUL (Remaining Useful Lifetime, RUL), and the data error is compared, showing that in the results of MAE, RMSE and MAPE three prediction error indicators. The prediction results of WOA-LSTM in B0005 and B0006 show the minimum prediction errors, which are 0.0563,0.0710,0.0415 in B0005 data set and 0.0583,0.0831,0.0454 in B0006 data set. Compared with the standard LSTM model, RNN model and GRU model, the error indexes of the model are decreased, which are 7 %, 4 % and 3 % respectively, which has great advantages. This method can provide a reliable predictive analysis method for battery design and fault diagnosis.