Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

• Published in: Microelectronics and reliability Vol. 75; pp. 142 - 153
• Main Authors: Song, Yuchen, Liu, Datong, Yang, Chen, Peng, Yu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2017
• Subjects: Dynamic modeling; Hybrid approach; Lithium-ion battery; Remaining useful life; Satellite; Lithium-ion battery; Satellite; Hybrid approach; Remaining useful life; Dynamic modeling
• DOI: 10.1016/j.microrel.2017.06.045
• ISSN: 0026-2714

Electrical power system (EPS) is one of the most critical sub-systems of the spacecraft. Lithium-ion battery is the vital component is the EPS. Remaining useful life (RUL) prediction is an effective mean to evaluate the battery reliability. Autoregressive model (AR) and particle filter (PF) are two traditional approaches in battery prognosis. However, the parameters in a trained AR model cannot be updated which will cause the under-fitting in the long term prediction and further decrease the RUL prediction accuracy. On the other hand, the measurement function in the PF algorithm cannot be obtained in the long term prediction process. To address these two challenges, a hybrid method of IND-AR model and PF algorithm are proposed in this work. Compared with basic AR model, a nonlinear degradation factor and an iterative parameter updating method are utilized to improve the long term prediction performance. The capacity prediction results are applied as the measurement function for the PF algorithm. The nonlinear degradation factor can make the linear AR model suitable for nonlinear degradation estimation. And once the capacity is predicted, the state-space model in the PF is activated to obtain an optimized result. Optimized capacity prediction result of each cycle is utilized to re-train the regression model and update the parameters. The predictor keeps working iteratively until the capacity hit the failure threshold to calculate the RUL value. The uncertainty involved in the RUL prediction result is presented by PF algorithm as well. Experiments are conducted based on commercial lithium-ion batteries and real-applied satellite lithium-ion batteries. The results have high accuracy in capacity fade prediction and RUL prediction of the proposed method. The real applied lithium-ion battery can meet the requirement of spacecraft. All the experiments results show great potential of the proposed framework. •IND-AR model and empirical model are fused via state-space model in RPF.•Iterative updating is used to improve the prediction capability of ND-AR.•The proposed method is used in real applied satellite battery RUL prediction.