NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling
NOCE-Net is a deep nested sequence model that learns useful representations directly from raw battery operational data to predict long voltage responses. It standardizes diverse battery datasets into Full Equivalent Cycles (FECs) and uses a combination of transformers and GRUs to capture both short-term cycle dynamics and long-term degradation trends. This method achieves strong zero-shot performance, showing learned states encode transferable degradation physics.