Capacity estimation for lithium-ion battery via a novel health indicator extracted from partial constant voltage charging curve

Abstract

This paper develops a capacity estimation method via a novel health indicator (HI) extracted from partial constant voltage (CV) charging curve. Firstly, the CV charging curve is analyzed based on the Thevenin model, whose results indicate that the CV charging curves under different battery aging states can be theoretically described by the same functional form. Secondly, with CV charging curve analysis results, regarding the CV charging curve at the first cycle as original curve, and those at other cycles as fitted curves, the coefficient of determination between those two kinds of curves is computed through the early-stage CV charging data and innovatively taken as HI for further capacity estimation. Meanwhile, considering that the early-stage CV charging data may still be insufficient in real application, a simple subsequent current prediction method is proposed to ease HI extraction in the case of insufficient data based on the first data point of CV charging curve. Thirdly, a moving window-based HI processing method is developed to mitigate the negative influence of the possible current measurement disturbances on capacity estimation accuracy. Finally, the proposed method is verified through Underwriters Laboratories Inc. - Purdue University battery aging dataset, whose results demonstrate that both mean absolute error and root mean squared error of capacity estimation results achieved by the model established by only 12 pairs of data points in the premise of appropriately selected original curve can be roughly controlled less than 1% and 1.5% in the case of sufficient data and insufficient data, respectively.