Cross-Condition State-of-Health Estimation of Lithium-Ion Batteries via Degradation-Feature Constraints and Domain-Difference Gating

Vollständiger Abstract

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To address the limited generalization of lithium-ion battery state-of-health (SOH) estimation under unseen aging conditions, this study proposes a degradation-feature-constrained domain-difference gated method (DFC-DGGate). Cycle-level features are constructed from capacity, voltage, local statistics, and first-order degradation variations. Three branches, namely Local ET, Trend Ridge, and Robust Huber, are used to characterize local nonlinear mapping, global degradation trends, and robust estimation, respectively. Their outputs are fused by a condition-aware domain-difference gate and further smoothed to obtain continuous SOH estimates. Cell-wise and condition-wise validations are conducted on the XJTU dataset, and external testing is performed on the NASA dataset. Under XJTU condition-wise validation, DFC-DGGate achieves an RMSE of 14.3895%, while the best-performing baseline, SVR, achieves 5.7623%. The proposed framework remains more accurate than the global Trend Ridge branch (22.3915%) but does not outperform the strongest nonlinear baselines under the substantial Sim_satellite shift. In XJTU-to-NASA validation, the anchor-corrected external extension achieves RMSEs of 20.45% and 19.10% on NASA core and NASA clean, respectively, slightly outperforming ExtraTrees on both subsets.