Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade

Vehicle mass and road grade information is important to improve the control capability and further intellectualization of vehicles. With the aim of real-time estimation of mass and grade without additional sensors, a two-step estimator is proposed in this paper. In the first-step estimator, the recursive least square with dual forgetting factors is used to estimate the vehicle mass with the consideration of the time-varying rolling friction coefficient and system error. In the second-step estimator, the road grade is estimated using an extended Kalman particle filter. Based on the data of CarSim/MATLAB co-simulation, the proposed approach has faster convergence rate and better tracking accuracy on the premise of meeting the real-time requirements by comparison with other estimation algorithms. The performance of the estimator is finally validated by the vehicle road test, and the results show that the mass and grade are estimated with great accuracy and robustness under different road conditions.