Lithium-ion battery remaining useful life estimation based on nonlinear ar model combined with degradation feature

Datong Liu Yue Luo Yu Peng Jianqing Fan Michael Pecht

Statistics Theory and Methods mathscidoc:1912.43349

Annual Conference of the Prognostics and Health Management Society, 3, 1803-1836, 2012.9
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 lithiumion 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 datadriven 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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@inproceedings{datong2012lithium-ion,
  title={Lithium-ion battery remaining useful life estimation based on nonlinear ar model combined with degradation feature},
  author={Datong Liu, Yue Luo, Yu Peng, Jianqing Fan, and Michael Pecht},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113853354341909},
  booktitle={Annual Conference of the Prognostics and Health Management Society},
  volume={3},
  pages={1803-1836},
  year={2012},
}
Datong Liu, Yue Luo, Yu Peng, Jianqing Fan, and Michael Pecht. Lithium-ion battery remaining useful life estimation based on nonlinear ar model combined with degradation feature. 2012. Vol. 3. In Annual Conference of the Prognostics and Health Management Society. pp.1803-1836. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113853354341909.
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