Residual Remaining Useful Life Prediction Method for Lithium-Ion Batteries in Satellite With Incomplete Healthy Historical Data

Due to the strict requirements of satellite systems, accurate remaining useful life (RUL) prediction of the key components is very important to the reliability and security of satellite systems. Otherwise, a failure could lead to catastrophic consequences and enormous economic losses. Because of the complex structure of the satellite and its complex space environment, the factors that affect the satellite systems status are numerous. Moreover, as a result of the healthy historical data of key components in satellite are too few, which makes the traditional methods based on analysis model are not suitable for RUL prediction of key components in satellite. In this paper, in order to solve the RUL prediction problem of Lithium-ion batteries (LIBs) in satellite with incomplete healthy historical data, we propose an efficient RUL prediction method for key components of satellite, which is called Residual Remaining Useful Life Prediction Method (RRULPM), based on the study of Multivariate State Estimation Technique (MSET). The RRULPM is make up of degradation model based on MSET state estimation and criteria of failure based on historical degradation value, which is developed by improving MSET and combining the residuals with life cycle damage (LCD) prediction creatively when lacking healthy historical data. Experimental results demonstrate that the RRULPM is excellent to achieve the RUL prediction problems of LIBs through the actual in orbit telemetry data. Unlike previous RUL prediction methods, RRULPM provides good feasibility and effectiveness. This research can serve as guidance for prognostics and health management (PHM) of key components in satellite.