Electric Vehicle State Parameter Estimation Based on DICI-GFCKF

Abstract

To improve the estimation accuracy of the state parameters of distributed electric vehicles, a double inverse covariance intersection generalized fifth-order cubature Kalman filter (DICI-GFCKF) es-timation algorithm is proposed. Based on the fifth-order cubature Kalman filter algorithm, the generalized cubature rule is used to directly obtain the weight and cubature point of the algorithm. Then, the inverse covariance intersection (ICI) data fusion algorithm is introduced and combined with the generalized fifth-order CKF, and the double inverse covariance intersection-generalized fifth-order cubature Kalman filter is derived. The algorithm is applied to estimate the state parameters of distributed electric vehicles. Finally, the simulation and the vehicle experiment show that the algorithm not only improves the estimation accuracy and stability but also reduces the influence of the system model nonlinearity on the algorithm, and has good effectiveness and robustness.

Keywords

• Electric vehicles
• state parameter estimation
• generalized cubature rule
• ICI data fusion
• fifth-order CKF