A study on the vehicle state estimation for a distributed-drive EV based on LO-EKF algorithms

A study on vehicle state estimation is conducted for a distributed-drive electric vehicle in this paper. Firstly Luenberger observer (LO) is adopted to observe the road slope, which has significant effects on vehicle state estimation. Then extended Kalman filter (EKF) algorithm is used with the data information obtained from ESP sensor as observed value, the dynamics state variables of distributed-drive electric vehicle are estimated. Finally a Carsim-Matlab co-simulation is performed. The results show that the proposed vehicle state estimation algorithm based on LO and EKF is feasible and can well estimate the relevant dynamics state variables of vehicle with a rather high convergence speed.