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 (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).

Keywords Lithium-ion battery  Fusion prognostics  Data-driven prognostics  ND–AR  RPF

Complete article available from the publisher and to the CALCE Consortium Members.