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Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

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Abstract

The lithium-ion battery cycle life prediction with particle filter (PF) depends on the physical or empirical model. However, in observation equation based on model, the adaptability and accuracy for individual battery under different operating conditions are not fully considered. Therefore, a novel fusion prognostic framework is proposed, in which the data-driven time series prediction model is adopted as observation equation, and combined to PF algorithm for lithium-ion battery cycle life prediction. Firstly, the nonlinear degradation feature of the lithium-ion battery capacity degradation is analyzed, and then, the nonlinear accelerated degradation factor is extracted to improve prediction ability of linear AR model. So an optimized nonlinear degradation autoregressive (ND–AR) time series model for remaining useful life (RUL) estimation of lithium-ion batteries is introduced. Then, the ND–AR model is used to realize multi-step prediction of the battery capacity degradation states. Finally, to improve the uncertainty representation ability of the standard PF algorithm, the regularized particle filter is applied to design a fusion RUL estimation framework of lithium-ion battery. Experimental results with the lithium-ion battery test data from NASA and CALCE (The Center for Advanced Life Cycle Engineering, the University of Maryland) show that the proposed fusion prognostic approach can effectively predict the battery RUL with more accurate forecasting result and uncertainty representation of probability density distribution (pdf).

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Acknowledgments

This research work is supported partly by National Natural Science Foundation of China (61301205), Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT.NSRIF.2014017), Research Fund for the Doctoral Program of Higher Education of China (20112302120027), the twelfth government advanced research fund (51317040302). The author would also express his sincere thanks to Dr. Wei He at CALCE of The University of Maryland and Dr. Eden Ma at PHM Center of City University of Hong Kong for their help on the CALCE battery data set. The authors would also like to thank anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, 150080, China

    Datong Liu, Yu Peng & Limeng Guo

  2. Beijing System Design Institute of Electro-Mechanic Engineering, Beijing, 100854, China

    Yue Luo

  3. Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON, K1S 5B6, Canada

    Jie Liu

  4. CALCE, The University of Maryland, College Park, MD, 20742, USA

    Michael Pecht

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  1. Datong Liu
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  2. Yue Luo
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  3. Jie Liu
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  4. Yu Peng
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  5. Limeng Guo
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  6. Michael Pecht
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Correspondence to Datong Liu.

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Liu, D., Luo, Y., Liu, J. et al. Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm. Neural Comput & Applic 25, 557–572 (2014). https://doi.org/10.1007/s00521-013-1520-x

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