NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling

Learning effective representations from battery operational history is essential for predictive health management and control-oriented applications. Existing approaches rely on response-dependent engineered features or short early-life windows, limiting generalisability and precluding use in prospective settings where future responses are unavailable. We propose $\texttt{NOCE-Net}$, a deep nested sequence model that learns control-enabling representations directly from raw cyclic operational data. Our data pipeline, $\texttt{re-BatteryData}$, standardises heterogeneous aging datasets into semantically equivalent Full Equivalent Cycles (FECs) defined by absolute charge throughput, enabling end-to-end learning across diverse operational regimes. $\texttt{NOCE-Net}$ combines multi-patch transformer encoders for intra-cycle dynamics with a GRU backbone for inter-cycle state evolution, operating on approximately $700$ batteries across ten public datasets spanning over $300$ operational profiles. In zero-shot evaluation on unseen battery chemistries and usage patterns, $\texttt{NOCE-Net}$ achieves $13.7$% MAPE in voltage response prediction, outperforming strong long-sequence baselines by over $43$% while using $40 \times$ fewer parameters. Shape and component diagnostics of the learned hidden states reveal that the GRU's principal direction of evolution achieves Spearman $|\rho| \geq 0.95$ with the capacity fade trajectory on average across all datasets with monotone degradation profiles, including zero-shot test batteries, providing evidence that the representations encode degradation dynamics transferable to downstream tasks beyond battery response modelling.