A generalizable physics-informed neural network for lithium-ion battery SOH estimation utilizing partial charging segments

Accurate state of health (SOH) estimation is essential for the safe and reliable operation of lithium-ion batteries. However, existing methods face significant challenges, primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints, which limit adaptability to different chemistries and real-world conditions. To address these issues, this study proposes an approach that extracts features from segmented state of charge (SOC) intervals and integrates them into an enhanced physics-informed neural network (PINN). Specifically, voltage data within the 25 %–75 % SOC range during charging are used to derive statistical, time–frequency, and mechanism-based features that capture degradation trends. A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed, where Lasso regression enables sparse feature selection, and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss. This design adaptively balances data-driven accuracy with physical consistency, thereby enhancing estimation precision, robustness, and generalization. The results show that the proposed method outperforms conventional neural networks across four battery chemistries, achieving root mean square error and mean absolute error below 1 %. Notably, features from partial charging segments exhibit higher robustness than those from full cycles. Furthermore, the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries, temperatures, and C-rates. This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.