Unscented Kalman filter with a reduced number of sigma-points and its application to the estimation of battery state of charge

This paper presents a modified approach to implementing Sigma Point Kalman Filters (SPKFs), in which the Singular Value Decomposition (SVD) of the covariance matrix is used to generate sigma points for the unscented transform. The advantage of the proposed approach is that it requires just (n+1) instead of the usual (2n+1) sigma-points with only a minor reduction of precision. The proposed method for selecting the sigma-points to adjust the normal process parameters during the update phase of the filter can be intuitively visualized in the 2D case by plotting the covariance ellipsoid. The proposed reduced sigma-point filter achieved satisfactory performance in a case study involving state-of-charge (SoC) estimation for vehicular batteries when compared to the original SPKF implementation.