Generative-Enhanced Physics-Informed Neural Network for Lithium-Ion Battery State of Health Estimation

Accurate estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is crucial for ensuring safety and extending life. However, existing data-driven methods still face two challenges in practice, the lack of data and physical mechanism explanation. To address these issues, this study proposes a novel hybrid framework that integrates a conditional generative adversarial network with physics-informed neural network (cGAN-PINN). The aging feature-guided cGAN generates synthetic voltage and temperature data using SOH and normalized cycle number as conditional inputs, enhancing data diversity and preserving physical consistency. Subsequently, both synthetic and real data are fed into PINN, which incorporates the Verhulst electrochemical model to embed prior physical knowledge of capacity fade dynamics, thereby enabling simultaneous SOH prediction and parameter estimation. Results demonstrate that the synthetic data generated by cGAN effectively alleviates overfitting and enhances generalization capability, reducing root mean square percentage error (RMSPE) by 42.0% on the testing set. The framework achieves RMSPE and mean absolute error (MAE) of 0.269 and 1.158% on the testing set, demonstrating robust performance for battery health management under data scarcity.