Estimation of state‐of‐energy for lithium batteries based on dual adaptive particle filters considering variable current and noise effects

Abstract

Accurate estimation of the state-of-energy (SOE) of lithium batteries is of great significance for the stable operation of electric vehicles. However, the timevarying parameters of the equivalent model and the difficulty of describing the nonlinear characteristics between the open circuit voltage (OCV) and SOE limit the further improvement of SOE estimation accuracy. In this study, the fitting method of OCV-SOE curves is improved and dual adaptive particle filters are used to improve the estimation accuracy of SOE. To improve the accuracy of the OCV-SOE correspondence expression at the beginning and end of the discharge process, the curve fitting is accomplished using Gaussian polynomials, and the model equations based on the Gaussian function system are reestablished. The identification of the equivalent model parameters and SOE estimation are achieved simultaneously using two interrelated particle filters, which partially weaken the influence of the time-varying equivalent parameters on the SOE estimation accuracy. To further improve the convergence capability of the algorithm, a noise adaptation method based on the change of state estimation is added to the dual particle filters so that the noise variance meets the requirements of the algorithm throughout the discharge process. The test results of two different discharge conditions, dynamic stress test, and supplemental federal test procedure, verify the effectiveness of the proposed method.