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.