Uncertainty-oriented collaborative learning for on-orbit state-of-health estimation of satellite lithium-ion batteries considering multi-operating conditions

State-of-health is a critical indicator quantifying the performance degradation of satellite lithium-ion batteries. Due to the strictly limited operating conditions, extracting the health indicators based on the measurable parameters and mapping them to the battery capacity is the only approach estimating the on-orbit state-of-health of satellite lithium-ion batteries. In-orbit data typically lacks true capacity labels, constraining SOH estimation accuracy. Conversely, ground accelerated tests yield labeled data but under stress conditions that differ markedly from on-orbit environments. Bridging the gap between models trained on ground-based multi-condition data and the specific requirements of on-orbit estimation remains a critical challenge. To address these challenges, this paper proposes an on-orbit battery state-of-health estimation framework based on multi-operating conditions uncertainty-aware collaborative learning approach with adaptive weight aggregation. Firstly, considering the on-orbit limited operating conditions, three degradation features are extracted and fused to accurately represent the battery capacity fade. Secondly, a Bayesian Long-Short-Term-Memory network is established to represents the state-of-health degradation processes under multi-operating conditions, thereby quantitatively capturing the inherent uncertainties from different charge and discharge stresses. Finally, a collaborative learning method with adaptive weight aggregation is proposed to enable sustained and distributed collaborative training across diverse operational conditions using ground test data. This uncertainty-oriented collaborative learning paradigm allows new data from different operating conditions to be continuously incorporated and more clients to join the model training process. Therefore, the state-of-health estimation model remains adaptability and robustness against evolving degradation patterns. The performance of the proposed method is validated on both on-orbit simulation data and public datasets. It achieves the mean absolute error of 0.56% under on-orbit operating conditions. These results demonstrate that the proposed method offers a promising and practical solution for satellite lithium-ion battery state-of-health estimation.