An Ultra‐Early Prediction Framework for Battery Capacity Degradation Trajectories Using Visualized Single‐Cycle Data

This article proposes a lithium‐ion battery capacity degradation trajectory prediction framework using the voltage, current, and capacity curves extracted from a single cycle during the ultra‐early stage of the battery testing process. This framework is composed of three phases: image construction, knot prediction, and trajectory prediction. First, the voltage, current, and capacity curves are converted to images. Then, a convolutional neural network (CNN) model composed of three AlexNet blocks is employed to predict multiple knots on the trajectory. Finally, piecewise cubic Hermite interpolating polynomial (PCHIP) interpolation is implemented to construct the trajectory based on the predicted knots. The effectiveness of the proposed framework is demonstrated using the Severson dataset with 124 battery cells. For the 29 cells in the test set, the average trajectory prediction error is 46.7 cycles in terms of mean absolute error (MAE), 51.3 cycles in terms of root mean square error (RMSE), and 7.6% in terms of mean absolute percentage error (MAPE), respectively.