Battery SOC estimation based on strong tracking Kalman filtering
ZHAO Ya-ni
Abstract：In order to solve the poor accuracy problem of SOC estimation caused by the inaccurate lithium-ion battery model and sudden state changing, a strong tracking Kalman filtering algorithm with time varying fading factor was proposed. The equivalent two-order RC model for lithium-ion battery was identified by the HPPC test method, and the existing extended Kalman filtering principle and the proposed strong tracking Kalman filtering algorithm were compared and analyzed. Through combining the strong tracking principle and Kalman filtering algorithm as well as introducing the time varying fading factor, the proposed method can imperatively estimate the residual error to maintain the orthogonality and make the residual error satisfy the Gauss white noise characteristics. The simulation verification shows that compared with the extended Kalman filtering principle, the proposed strong tracking Kalman filtering algorithm has higher estimation accuracy under the condition of both inaccurate model and sudden state changing,and the estimation error is less than 2.5%,where the accuracy increases nearly by 45%.
Keywords：battery SOC estimationstrong tracking principleKalman filteringstrong tracking Kalman filteringtime-varying fading factororthogonality principlelithium-ion battery modelelectrical vehicle
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 192-197 )