Accurately estimation of the state of health (SOH) of satellite lithium-ion batteries using in orbit measurable parameters is crucial for the safe and reliable operation of satellites. For the SOH estimation of satellite lithium-ion batteries, both performance degradation characterization and assessment should be self-adaptive to different operating conditions. Therefore, to address the insufficient validity of characterization parameters and the reliability of assessment results caused by uncertainty in the model and data for satellite lithium-ion batteries under different operating conditions, this paper proposes a probabilistic SOH estimation method based on BNN-PF. Extracting different health indicators (HI) from measurable parameters during satellite lithium-ion batteries charging process to characterize performance degradation, combining Permutation Entropy with principal component analysis to improve feature recognition ability for different tasks. Furthermore, a Bayesian neural network (BNN) is applied to infer the SOH of lithium-ion batteries and quantify uncertainty. The uncertainty obtained by integrating empirical model with particle filter (PF) algorithms further enhances the adaptability of the proposed method to different operating conditions. The experimental results illustrate that the method proposed in this paper demonstrates good adaptability and universality for SOH estimation of satellite lithium-ion batteries under different operating conditions. The cross-validation test results show that the maximum estimation error is less than 0.01, and the estimation interval coverage of most results is higher than 0.95, indicating that the method has good prospects in spatial application scenarios.