Residual life prediction of lithium-ion batteries based on data preprocessing and a priori knowledge-assisted CNN-LSTM

Abstract

Lithium-ion batteries have become widely used in many industries due to their outstanding performance, making it vital to accurately predict the remaining useful life (RUL) of these batteries. This will aid in developing energy allocation strategies and ensure the safe use of lithium batteries. To overcome the issue of inaccurate RUL prediction, a new method is proposed that leverages data preprocessing and a prior knowledge-assisted convolutional neural network-long short-term memory neural network (CNN-LSTM). This method utilizes capacity as the health factor and employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the capacity sequence, eliminating noise components through data reconstruction. The reconstructed capacity sequence data are then used to pretrain the CNN-LSTM neural network, forming a priori knowledge. Finally, real-time battery capacity data are used to train the prior knowledge-aided CNN-LSTM neural network for real-time RUL prediction of Lithium-ion batteries. The results show that this method significantly improves the RUL prediction accuracy and reduces the prediction error while being more robust than existing methods.