Generalizable battery representation learning from operational data

Establish effective battery representation learning from real-world operational data that captures battery-type-aware, transferable dynamics across long horizons and diverse operating conditions.

Background

The paper motivates representation learning that can support predictive health management, degradation monitoring, and control-oriented applications without relying on future battery responses or manually engineered response-dependent features. It identifies the heterogeneity of battery datasets, path-dependent degradation, limited operational diversity, and dependence on disruptive reference performance tests as barriers to learning broadly generalizable representations.

The authors explicitly characterize the broader objective of learning effective representations from real-world battery data as unresolved. The proposed NOCE-Net framework addresses this challenge empirically through nested operational-context modelling, but the general challenge is not claimed to be fully solved.

References

However, learning such representations from real-world battery data remains an open challenge.

— NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling  (2610.21391 - Bhattacharjee et al., 15 Oct 2026) in Section 1, Introduction

Stronger real-world validation under such settings therefore remains an open challenge.

— NOCE-Net: Representation Learning for Battery Operational Context via Nested Sequence Modelling  (2610.21391 - Bhattacharjee et al., 15 Oct 2026) in Section 5, Conclusion and Limitations