Estimation of Vehicle Mass Using an Extended Kalman Filter

Vehicle mass is an important parameter when developing features which improve drivability and performance feel for passenger cars. A vehicle’s mass naturally depends on the load and the number of passengers. Therefore it is desired to have a fast, accurate and robust mass estimation algorithm. In this thesis an extended Kalman filter is used to estimate the mass of a passenger car. The filter uses the wheel torque, vehicle speed and road slope as input, where the road slope is measured by an accelerometer. The estimation algorithm is tested on real data and in simulated environments. The results show that the filter is fast and sufficiently accurate; it often reaches within 5% of the true mass after a few seconds. However, the results also show how changes in the environmental parameters can reduce the possibilities of getting an accurate estimation.