Lithium-ion Battery Remaining Useful Life Estimation Based on nonlinear AR Model combined with Degradation Feature

Datong Liu, Yue Luo, Yu Peng, Xiyuan Peng, and Michael Pecht
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Research Fund for the Doctoral Program of Higher Education of China
phmc_12_131.pdf301.27 KBSeptember 18, 2012 - 4:40pm

Long term prediction such as multi-step time series prediction is a challenging prognostics problem. This paper proposes an improved AR time series model called ND-AR model (Nonlinear Degradation AutoRegression) for Remaining Useful Life (RUL) estimation of lithium-ion batteries. The nonlinear degradation feature of the lithium-ion battery capacity degradation is analyzed and then the non-linear accelerated degradation factor is extracted to improve the linear AR model. In this model, the nonlinear degradation factor can be obtained with curve fitting, and then the ND-AR model can be applied as an adaptive data-driven prognostics method to monitor degradation time series data. Experimental results with CALCE battery data set show that the proposed nonlinear degradation AR model can realize satisfied prognostics for various lithium-ion batteries with low computing complexity.

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Submission Keywords: 
Remaining Useful Life Estimation
Lithium-ion battery
time series
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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