A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model

Abstract

Practical open-loop capacity estimation models, such as the Arrhenius aging model, require parameter updating for the real-world capacity estimation in order to guarantee the estimation accuracy. In this paper, a novel capacity estimation method for lithium-ion batteries, based on the fusion estimation of charging curve sections and the discrete Arrhenius aging model using sequential extended Kalman filters, is proposed. The estimation method based on fractional charging curves is developed to estimate the battery capacity during vehicle charging, and the estimation results serve as the feedback using the first Kalman filter to update the model parameters of the discrete Arrhenius aging model. Then, the second Kalman filter makes a fusion capacity estimation based on the results of charging curve sections and the discrete Arrhenius aging model with the modified parameters. The results of the cycle life tests show that the proposed algorithm can modify the parameters of the discrete Arrhenius aging model online. And the fusion capacity estimation error is less than 1% when the model parameters reach a steady state.