Prediction of Failure in Lithium‐Rich Cathode Half‐Cells Using Early‐Cycle Data

Abstract

Lithium‐ion batteries have attracted sustained attention because of their wide applications in energy storage systems. In laboratory research and development, the electrochemical performance of electrode materials is commonly evaluated using half‐cell configurations. However, half‐cell cycling tests are time‐consuming and involve certain safety risks, which may constrain the rapid iterative development of electrode materials. Therefore, an efficient early warning method for half‐cell failure is of practical interest. In this work, a dataset was constructed from electrochemical cycling data of half‐cells based on lithium‐rich cathode materials, and a grid‐search‐optimized gradient boosting decision tree model (GBDT) was developed for early failure prediction. Statistical features, including the mean, standard deviation, and decay rate, were extracted from the first 50 cycles to convert high‐dimensional time‐series data into structured descriptors, thereby achieving effective dimensionality reduction. Using only the mean and standard deviation of specific discharge capacity from the first 50 cycles, the GBDT model achieved the best performance, with accuracies of 0.98 and 0.96 on the training and test sets, respectively. At a current density of 1C, failure identification was also achieved using the mean specific discharge energy as a single feature, with an accuracy of 0.90. These results demonstrate the feasibility of early failure prediction based on limited early‐cycle data and may provide a certain reference value for half‐cell evaluation in laboratory scenarios and for the lightweight development of emerging electrode materials.