Neural Computing and Applications, Volume 25, Issue 3-4, pp 557-572, September 2014

Lithium-Ion Battery Remaining Useful Life Estimation Based on Fusion Non-linear Degradation AR Model and RPF Algorithm

Datong Liu, Yue Luo, Jie Liu, Yu Peng, Limeng Guo and Michael Pecht

Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
Beijing System Design Institute of Electro-Mechanic Engineering, Beijing 100854, China
Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
CALCE, The University of Maryland, College Park, MD 20742, USA

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 non-linear degradation feature of the lithium-ion battery capacity degradation is analysed, and then, the non-linear accelerated degradation factor is extracted to improve prediction ability of linear AR model. So an optimized non-linear degradation autoregressive (NDľAR) time series model for remaining useful life (RUL) estimation of lithium-ion batteries is introduced. Then, the NDľAR 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).

Keywords Lithium-ion battery  Fusion prognostics  Data-driven prognostics  NDľAR  RPF

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