Research
STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation
The paper introduces STEK, a framework for retrospective battery multi-target estimation that combines cell-level lifecycle dQ/dV terrain with endpoint-derived structural proxies via quality-proxy-conditioned gating. It uses bounded FiLM and a validity-gated residual head to refine endpoint-curve representations, plus a Koopman-inspired low-rank linear map for cross-channel consistency. Experiments under the MIX-battery protocol show improvements over endpoint-only baselines and direct concatenation, with graceful fallback when descriptors are removed.
battery health estimationmachine learningKoopman theory