Adaptive sigma Kalman filter method for state-of-charge estimation based on the optimized battery model

Abstract

Taking into account the equivalent coulomb efficiency and polarization effect, the nonlinear optimal compensative state and observation models were applied for state of charge (SoC) estimation of the lithium iron phosphate battery. On the basis of battery's parameter identification, an adaptive sigma Kalman filter algorithm was proposed to improve the accuracy of the SoC estimation and reduce the computational complexity of the traditional extended Kalman filter algorithm. The square root of state estimation error covariance was introduced to improve the positive semi-definition of the state covariance. Meanwhile, the estimated state variable and the observed variable were updated based on the iterative minimum mean square error estimation to achieve a precise estimate of the battery's SoC. Experiments were built, and the results indicate that the proposed optimized battery model and the SoC estimation algorithm are accurate and effective.