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…

• KI
  Korea Institute of Energy Research