Aging trajectory and end-of-life prediction for lithium-ion battery via similar fragment extraction of capacity degradation curves

Abstract

Considering the limitations on future general deterioration trend description and local capacity regeneration capture of existing methods, this paper designs a novel aging trajectory and end-of-life (EOL) prediction method for lithium-ion battery via similar fragment extraction of capacity degradation curves. The proposed method attempts to make the following three contributions: 1) By fusion of particle filter (PF)-based general deterioration trend description and similar fragment extraction-based local capacity regeneration capture, the developed method can achieve accurate aging trajectory and EOL prediction. 2) Considering the similarity in degradation paths, a novel dynamic time warping-based similar fragment extraction method is proposed to learn the deterioration path from the same type batteries' limited capacity degradation curves. 3) On the basis of the predicted future capacity, the PF-based capacity empiric degradation model can be iteratively updated to accurately reflect battery future general deterioration trend. The validation results based on Beijing Institute of Technology dataset demonstrate that the maximum mean absolute error and root mean squared error of predicted aging trajectory, and the maximum value of absolute relative error between reference and predicted EOL achieved by the developed method with the help of the same type of battery's limited capacity degradation curves are only 1.09%, 1.28% and 3.23%, respectively.