Real-Time Vehicle Parameters Estimation

Parameter estimation for vehicle systems is in general a challenging topic from both sensor instrumentation and modeling perspectives. Modeling vehicle systems is a rather complex process, especially considering the numerous unknown effects on the system such as, for example, aerodynamic effects, road grade and bank angles, roll and pitch kinematics, and suspension nonlinearities. This study develops a method that is able to estimate several vehicle parameters with high accuracy for regular driving behavior. The parameter estimations are performed using the polynomial chaos-based extended Kalman filter (gPC-EKF). This method is a computationally efficient, derivative free, iterative, nonlinear regression technique which is able to estimate multiple parameters in real time. The paper presents the results obtained for estimating the location of the CG of the vehicle in the horizontal plane, and the sprung mass of the vehicle using the proposed technique. Real test data have been used for validation purposes.