Cubature kalman filter-based state estimation for distributed drive electric vehicles

Improvement of electric vehicle handling stability and active safety depends strongly on real-time knowledge of important vehicle states in vehicle dynamics control systems, which is difficult to measure directly for both technical and economic reasons in fact. This paper presents a novel method to estimate critical vehicle states such as vehicle sideslip angle and lateral tire forces by utilizing real-time measurements only from sensors that have already been integrated in modern cars. By considering load transfer and nonlinear Dugoff tire, the four-wheel nonlinear vehicle dynamics model with the eight degrees of freedom is built. To address vehicle dynamics system nonlinearities, the vehicle state estimator based on cubature kalman filter is derived and investigated. Simulation with double lane change manoeuvre is carried out in the platform of Matlab/Simulink-Carsim. It is found from the simulation results that the proposed estimator can effectively estimate the vehicle sideslip angle and lateral tire forces.