State Parameter Estimation of Distributed Drive Electric Vehicle Based on Generalized fifth-order CKF Algorithm

To improve the estimation accuracy of distributed electric vehicle state parameters, a generalized fifth-order cubature Kalman filter (GHCKF) is proposed. Based on the fifth-order cubature Kalman filter algorithm, the weights and cubature points of the algorithm are obtained by using the generalized cubature rule, then use singular value decomposition (SVD) instead of the traditional Cholesky decomposition, derive the generalized fifth-order cubature Kalman filter (GHCKF), and apply the algorithm to the estimation of the state parameters of electric vehicles. Finally, the simulation shows that the algorithm not only improves the estimation accuracy and algorithm system stability but also reduces the influence of system model nonlinearity on the algorithm, which has better effectiveness and robustness.