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

Abstract 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.

[1]  J. D. Kozlowski Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[2]  J. Shim,et al.  Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature , 2002 .

[3]  Jianqing Fan,et al.  Nonlinear Time Series : Nonparametric and Parametric Methods , 2005 .

[4]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.

[5]  Yoshitsugu Sone,et al.  Cycle-life testing of 100-Ah class lithium-ion battery in a simulated geosynchronous-Earth-orbit satellite operation , 2006 .

[6]  Bhaskar Saha,et al.  An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries , 2010 .

[7]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[8]  Fan Li,et al.  A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter , 2015, Microelectron. Reliab..

[9]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[10]  H. Akaike Fitting autoregressive models for prediction , 1969 .

[11]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[12]  Miaohua Huang,et al.  Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model , 2016, Microelectron. Reliab..

[13]  Marcos E. Orchard,et al.  Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring , 2017 .

[14]  Jean-Michel Vinassa,et al.  Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications , 2012, Microelectron. Reliab..

[15]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[16]  Dong Wang,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter , 2016, IEEE Transactions on Instrumentation and Measurement.

[17]  Xiaofeng Wang,et al.  Lebesgue-Sampling-Based Diagnosis and Prognosis for Lithium-Ion Batteries , 2016, IEEE Transactions on Industrial Electronics.

[18]  B. Friedlander,et al.  The Modified Yule-Walker Method of ARMA Spectral Estimation , 1984, IEEE Transactions on Aerospace and Electronic Systems.

[19]  W. Wang,et al.  A data-model-fusion prognostic framework for dynamic system state forecasting , 2012, Eng. Appl. Artif. Intell..

[20]  Liu Qiao,et al.  Automotive battery management systems , 2008, 2008 IEEE AUTOTESTCON.

[21]  George J. Vachtsevanos,et al.  Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems , 2010 .

[22]  Xiaojuan Wu,et al.  Fault diagnosis and prognostic of solid oxide fuel cells , 2016 .

[23]  K. Goebel,et al.  An integrated approach to battery health monitoring using bayesian regression and state estimation , 2007, 2007 IEEE Autotestcon.

[24]  Chen Lu,et al.  Li-ion battery capacity estimation: A geometrical approach , 2014 .

[25]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[26]  Chao Hu,et al.  Online estimation of lithium-ion battery capacity using sparse Bayesian learning , 2015 .

[27]  Marc Doyle,et al.  Mathematical Modeling of the Lithium Deposition Overcharge Reaction in Lithium‐Ion Batteries Using Carbon‐Based Negative Electrodes , 1999 .

[28]  Michael Buchholz,et al.  Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .

[29]  Jie Gu,et al.  Prognostics implementation of electronics under vibration loading , 2007, Microelectron. Reliab..

[30]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[31]  Datong Liu,et al.  Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning , 2015 .

[32]  H. Akaike A new look at the statistical model identification , 1974 .

[33]  Kwok L. Tsui,et al.  A naive Bayes model for robust remaining useful life prediction of lithium-ion battery , 2014 .

[34]  Kang-In Rhee,et al.  Reductive leaching of cathodic active materials from lithium ion battery wastes , 2003 .

[35]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

[36]  Michael Buchholz,et al.  State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation , 2011 .

[37]  Robert H. Shumway,et al.  Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples , 1990 .

[38]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[39]  Jie Liu,et al.  Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm , 2013, Neural Computing and Applications.

[40]  Zhen Liu,et al.  An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries , 2013, Microelectron. Reliab..

[41]  M. Yoshio,et al.  Lithium-ion batteries , 2009 .

[42]  Zhigang Tian,et al.  A framework for predicting the remaining useful life of a single unit under time-varying operating conditions , 2013 .

[43]  Yu Peng,et al.  Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..

[44]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[45]  Douglas Bender The Open-Loop Transfer Matrix of an Estimator-Controller , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[46]  Robert Kostecki,et al.  Diagnostic Characterization of High Power Lithium-Ion Batteries for Use in Hybrid Electric Vehicles , 2001 .

[47]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .