Battery Model Parameters Estimation with the Sigma Point Kalman Filter

Abstract

Accurate estimation of the State of Charge (SOC) of the battery is one of the key problems to the battery management system. The SOC should be obtained indirectly according to some algorithms under a mathematical model, along with some measurable quantities. A Sigma Point Kalman Filter based battery model parameters estimation method is proposed. The parameters can be estimated accurately while efficiently with the proposed method. Compared to the classical least squares method, the proposed method consumes much less memory and calculation time, which makes it suitable for embedded applications.