A high fidelity system for estimating the remaining useful life (RUL) for Li-ion batteries for aerospace applications is presented. The system employs particle filtering coupled with outlier detection to predict RUL. Calculations of RUL are based on autonomous measurements of the battery state-of-health by onboard electronics. Predictions for RUL are fed into a maintenance advisor which allows operators to more effectively plan battery removal. The RUL algorithm has been exercised under stressful conditions to assert robustness.