A Physics-Informed Event-Triggered Learning Approach to Long-Term Spacecraft Li-Ion Battery State-of-Charge Estimation

In this work, an event-triggered learning problem for state-of-charge (SOC) estimation of long-term spacecraft li-ion batteries with system aging and disturbances is investigated based on a physics-informed long short-term memory (PI-LSTM) network. An equivalent circuit model and a pretrained Gaussian process regression model are integrated into a long short-term memory (LSTM) network, which is trained and updated quickly with limited transmission data. By considering noisy data and physical constraints simultaneously, the PI-LSTM approach provides interpretable dynamic models for the long-term battery SOC estimation. Then, an unscented Kalman filter is proposed to estimate the SOC performance. By using weighted average voltage prediction errors, an event-triggering condition is established to guarantee the estimation performance with a reduced signal transmission rate. The effectiveness of the proposed approach is validated through experiments on a real spacecraft Li-ion battery platform, which achieves the SOC estimation error of less than 2%, and the maximum voltage prediction error is reduced by 61% after updating the PI-LSTM model.