Universal Battery Capacity Degradation Forecasting Driven by Foundation Models Across Diverse Chemistries and Conditions

ABSTRACT Accurate forecasting of battery capacity fade is essential for the safety, reliability, and long‐term efficiency of energy storage systems. However, the heterogeneity across cell chemistries, form factors, and operating conditions makes it difficult to build a single model that generalizes beyond its training domain. This work proposes a unified capacity forecasting framework that maintains robust performance across diverse chemistries and usage scenarios. We curate 20 public aging datasets into a large‐scale corpus covering 1704 cells and 3 961 195 charge–discharge cycle segments, spanning temperatures from to , multiple C‐rates, and application‐oriented profiles such as fast charging and partial cycling. On this corpus, we adopt a Time‐Series Foundation Model (TSFM) backbone and apply parameter‐efficient Low‐Rank Adaptation (LoRA) together with physics‐guided contrastive representation learning to capture shared degradation patterns. Experiments on both “known” and held‐out “unknown” datasets show that a single unified model achieves competitive or superior accuracy compared with recent state‐of‐the‐art time‐series forecasting methods trained separately for each dataset, while retaining stable performance on chemistries, capacity scales, and operating conditions excluded from training. These results demonstrate the potential of TSFM‐based architectures as a scalable and transferable solution for capacity degradation forecasting in battery management systems.