Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

Abstract

With the in-depth study of electrochemical models, the use of reduced-order electrochemical models on battery management systems becomes a possible future trend. In this paper, a two-step parameter identification method and the square-root cubature Kalman filter are employed. The pseudo-spectral method is used to solve the solid-phase diffusion equation, while the liquid-phase concentration equation is simplified by the Galerkin method. The reduced-order model maintains high accuracy while significantly reducing computational burden, the computation of a discharge voltage is within 1.5 s, which makes it possible to use the model for parameter identification and real-time state estimation. Next, the ant lion optimizer is applied to identify 21 parameters in the electrochemical model and 4 parameters in the thermal model respectively. Identification and validation results in constant current discharge and several dynamic tests demonstrate the high accuracy of the reduced-order model. Finally, the square-root cubature Kalman filter based on the identified parameters is developed to estimate the state of charge and temperature. The states are initialized with certain errors in two dynamic tests to verify the performance of the algorithm. The results show that the designed state observer has good convergence, robustness, and accuracy.