Physics-Informed Neural Network for Satellite Battery Degradation Estimation Based on an Extreme Segment Data Within 2 Min

Accurately estimating the state of health (SOH) of satellite batteries is crucial for reliable satellite operation and safe decision-making. However, the complex degradation processes of batteries in space, coupled with limited labeled data, pose challenges for existing SOH estimation methods. We propose a physics-informed neural network (PINN) for satellite battery SOH estimation using only an extreme segment data within 2 min. To address battery degradation complexity, we model degradation attributes from an empirical and state-space perspective. We combine neural networks with physical principles to capture the degradation dynamics of batteries, enabling accurate SOH estimation. To tackle limited and inconsistent data acquisition per cycle, health features are constructed from relaxation voltage data within 2 min, ensuring independence from charging/discharging strategies. Unique factors specific to satellite operations are also considered. To validate the proposed method, the battery degradation experiments simulating a geostationary earth orbit (GEO) satellite are conducted, and an aging dataset is generated. The proposed method achieves a mean absolute percentage error (MAPE) of 1.33% for SOH estimation using a 2-min extreme data segment. Our studies provide valuable insights into battery prognosis and health management for space missions, aiding in optimizing battery performance and longevity in satellite operations.