Physics-Informed Neural Network for Spacecraft Lithium-Ion Battery Modeling and Health Diagnosis

The modeling of lithium-ion batteries is essential for the battery management. In space operating conditions, no additional tests can be performed, and the battery is usually not discharged to empty, resulting in less data available for analysis. Furthermore, the battery characteristics are influenced by factors, such as service time and temperature, so the model should be able to be updated with limited data. This article investigates a novel physics-informed neural network (PINN) model, which integrates the equivalent-circuit model (ECM) into the recurrent neural network (RNN) framework to reconstruct RNN structure. The integration of ECM adds physical constraints to the training of RNN, so that the model conforms to both the physical knowledge and data distribution, which reflects the impact of environment and aging. The approach is validated through simulation and experiments based on a real spacecraft lithium-ion battery cell. Results indicate that the proposed model outperforms a white-box model and an enhanced ECM on real-time modeling and discharge profiles prediction. Moreover, the battery state of health is estimated based on the parameters identified by this method, with an error of less than 2%.