Sequence-Conditioned Physics-Informed Neural Network for Lithium-Ion Battery State of Health Estimation Using Charging Segments at High State of Charge

Abstract

Accurate and reliable state of health (SOH) estimation is essential for lithium-ion battery safety and lifetime management. Physics-informed neural networks (PINNs) integrate battery degradation knowledge with deep learning to improve the physical consistency of SOH estimation. However, existing PINNs insufficiently capture degradation evolution across consecutive cycles and show limited applicability across battery chemistries and charge/discharge protocols. To address these issues, we propose a sequence-conditioned physics-informed neural network (SC-PINN) that uses a gated recurrent unit (GRU) to encode local degradation states and incorporates a degradation dynamics consistency constraint and a monotonic degradation prior to improve prediction accuracy and physical plausibility. We also develop a general charging feature extraction method for different battery chemistries and charge/discharge protocols, extracting unified 18-dimensional health features from charging segments at high state of charge (SOC). The method is validated using 317 batteries and 197,024 valid cycle samples covering four battery chemistries across nine representative tasks. SC-PINN achieves a mean absolute percentage error (MAPE) of 0.6285% and a root mean square error (RMSE) of 0.8924%, with the lowest RMSE in eight of nine tasks. Compared with other methods, the proposed method demonstrates good stability and applicability in both standard and small-sample experiments.