Robust and data-efficient battery state of charge estimation via transfer learning-enhanced physics-informed neural networks

Accurate state of charge (SOC) estimation is essential to ensure the reliability and safety of battery management systems. Conventional data-driven methods rely on large labeled datasets and exhibit poor generalization across battery chemistries. By contrast, physics-based models offer interpretability but require complex parameter identification and incur high computational costs. To address these challenges, this study proposed a transfer learning-enhanced physics-informed neural network (TL-PINN) framework that combined the interpretability of physics-based models with the adaptability of deep learning. The framework integrated three key components: a physics-informed neural network for SOC estimation, a TL mechanism for domain adaptation, and a hybrid loss function that balances physical consistency and data efficiency. Experimental evaluations on the nickel manganese cobalt, lithium polymer, and nickel cobalt aluminum cells demonstrated that the TL-PINN outperformed conventional methods by achieving a lower root mean square error (RMSE), faster inference (approximately 2.4 ms), and enhanced robustness to sensor noise. Notably, under data-scarce conditions, the TL-PINN reduced the RMSE by more than 63% compared with the extended Kalman filter and more than 50% compared with recurrent neural network models. Furthermore, it consistently maintained a prediction error within 2%, even with noisy measurements. These results validate TL-PINN as a robust and scalable solution for SOC estimation under practical constraints and suggest its potential extension to broader battery health diagnostics, such as SOH and remaining useful life estimation. • A novel TL-PINN framework that integrates physical constraints with transfer learning for enhanced SOC estimation. • A cross-chemistry generalization study showing the model's ability to adapt to different battery types without retraining. • Evaluation under limited labeled data and noisy measurements, demonstrating superior data efficiency and noise robustness. • A comprehensive comparison with baseline models across various practical scenarios.