A comparative study on identification of vehicle inertial parameters

摘要

This paper presents a comparative analysis of different analytical methods for identification of vehicle inertial parameters. The effectiveness of four different identification methods namely Recursive Least Squares (RLS), Recursive Kalman Filter (RKF), Gradient, and Extended Kalman Filter (EKF) for estimation of mass, moment of inertia and location of center of gravity of a vehicle is investigated. Requirements, capabilities and drawbacks of each method for real time applications are highlighted based on a comprehensive simulation analysis using CarSim. The Extended Kalman Filter method is shown to be the most reliable method for online identification of vehicle inertial parameters for active vehicle control, vehicle stability, and driver assistant systems.