Research on Battery Life prediction Based on Deep Learning

In order to improve the accuracy of life prediction of lithium-ion battery, a life prediction method based on deep belief network and long short-term memory network is proposed. Combined with the capacity data of lithium-ion battery from the Prognostic Center of Excellence (PCoE) of NASA Ames Research Center, discuss the prediction accuracy of the network under different hidden layers, different hidden layer nodes and different optimizers, then choose the best for the fusion model. Combining the above two models, the experimental results show that: the fused model can effectively fit the decline trend of battery capacity, and the remaining useful life prediction error is only 1 cycle. Compared with using DBN or LSTM alone, the prediction accuracy of fusion model is better.