Research on Joint Estimation Algorithm of Intelligent Vehicle Mass and Road Grade

Abstract

Accurate estimation of vehicle mass and road grade is of great significance for vehicle dynamics, safety, comfort and economy. For intelligent vehicles, the traditional methods use less data, and the estimated vehicle pitch angle is directly used as the road grade, so their accuracy is limited, especially in road grade estimation. Therefore, a joint estimation algorithm of intelligent vehicle mass and road grade is proposed. Firstly, the unscented kalman filter is used to estimate the vehicle pitch angle, and uses the derived vehicle pitch angle-road grade relation to calculate the road grade. Then, the vehicle mass is estimated by the vehicle longitudinal dynamics model. Finally, the estimated values of vehicle mass and road grade are cross-iterated. In order to verify the effectiveness of the algorithm, a simulation experiment platform was built by using Carsim and Matlab/Simulink software. Experimental results show that the proposed algorithm has fast convergence speed, high accuracy and good stability.