Research on Lithium battery life prediction model based on CNN-GRU combined neural network - ZHANG An′an; XIE Linxing; YANG Wei (Diance yu yibiao)
It is difficult to obtain direct performance parameters such as lithium battery capacity and internal resistance, which leads to the problem of low accuracy of lithium battery life prediction. A lithium battery life prediction model based on a combined neural network of convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed. Four indirect health factors including constant current charging time interval, constant voltage charging time interval, discharging temperature peak time and cycle times are extracted from lithium battery charging and discharging experiments, and the Pearson and Spearman correlation coefficients are established. And then, a lithium battery life prediction model is built based on CNN-GRU combined neural network. Finally, the rationality of extracting health factors is verified by actual data, and the prediction results are compared with SVR model, long short-term memory (LSTM) model, GRU model, and CNN-LSTM model to verify the superiority and effectiveness of the proposed model.