Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network

Accurate battery lifespan prediction can guarantee battery safety in applications. Considering the complex nonlinear nature of battery degradation, it is more challenging to predict battery aging trajectories utilizing less data. A two-stage prediction method for battery capacity aging trajectories is proposed. The first stage is battery lifespan prediction based on the Siamese-Convolutional neural network (Siamese-CNN). Taking the predicted lifespan as its prior information, the second stage is predicting capacity aging trajectories with the Convolutional neural network (CNN). The method only requires partial voltage-current data from 30 cycles, of which the similarity in different batteries is obtained by the Siamese-CNN. The lifespan-unknown batteries in the testing set are fed into the Siamese-CNN to match the lifespan-known training set battery with the highest similarity. Then, CNN is employed to predict the capacity aging trajectories under different profiles. The ratio of each cell's predicted lifespan to the fixed length of the CNN output sequence is regarded as the interval for uniform sampling to keep the series consistent. The prediction accuracy of the method is validated by an open dataset. It is demonstrated that the battery lifespan and capacity aging trajectory prediction achieve a mean absolute percentage error (MAPE) of 2.44% and 1.28%, respectively. •A Siamese-CNN is employed for battery lifespan prediction and capacity aging trajectory.•Only partial voltage-current data from the first 30 cycles are required.•Achieved 2.44% test error for battery lifespan prediction and 1.28% for capacity aging trajectory prediction.