Two-layer structure algorithm for estimation of commercial vehicle mass

Abstract

Aiming at the problem of mass estimation for commercial vehicle, a two-layer structure mass estimation algorithm was proposed. The first layer was the grade estimation algorithm based on recursive least squares method and the second layer was a mass estimation algorithm using the extended Kalman filter. The estimated grade was introduced as the observation quantity of the second layer. The influence of the suspension deformation on grade estimation was considered in the first layer algorithm, which was corrected in real time according to the mass and road grade estimated by the second layer algorithm. The proposed estimation algorithm was validated via a co-simulation platform involving TruckSim and MATLAB/Simulink. Finally, a road test was carried out, and the evaluation method using the root mean square error was proposed. According to the test, the average value of the root mean square error reduces from 871.65 to 772.52, grade estimation is more accurate, and the convergence speed of mass estimation is faster, compared with estimation results of the extended Kalman filter method.