Early-Cycle Lifetime Prediction of Lithium-Ion Batteries with Ultra-Short Cycle Life Using Transferable Statistical Features

Early-cycle lifetime prediction of lithium-ion batteries is important for rapid cell screening, battery development, and manufacturing quality control. However, accurate prediction at the early stage remains difficult because capacity fade is usually very limited during the initial cycles, and the available degradation signals are weak. In this study, an early degradation voltage morphology (EDVM)-based framework is proposed for early cycle-life prediction. Two statistical features and one degradation mode voltage signature (DMVS) feature are extracted from the discharge capacity-difference profiles between the 10th and 3rd cycles and combined with an extreme gradient boosting (XGBoost) model. Validation on 138 commercial NCM811 cylindrical cells shows that the proposed framework achieves a mean absolute percentage error (MAPE) of 12.29% using only the first 10 cycles of data. In addition, the DMVS feature identifies three groups of early degradation behavior and provides physically interpretable information on degradation heterogeneity. These results indicate that the proposed method is an efficient and interpretable approach for early cycle-life prediction and has practical potential for battery evaluation and screening.