To solve the problem that control system of the intelligent vehicle is hard to measure the vehicle mass and road gradient, this paper built a longitudinal dynamics model of vehicle. Based on theoretical model, discrete steady-state Kalman filter was used to estimate gradient of slope and vehicle mass, and simulation platform was established by Carsim and Maltab/Simulink to verify the accuracy and instantaneity of the algorithm. A proper acceleration sensor was selected, according to the stable Kalman filter theory. A real test was conducted, and the instantaneity and accuracy of this method for vehicle mass and road slope was verified by comparing with the data from inertial navigator.
Estimation of vehicle mass and road slope based on steady-state Kalman filter
2017-10-01
733424 byte
Conference paper
Electronic Resource
English
Road Slope and Vehicle Mass Estimation Using Kalman Filtering
Taylor & Francis Verlag | 2002
|Road Slope and Vehicle Mass Estimation Using Kalman Filtering
British Library Conference Proceedings | 2003
|Road slope and vehicle mass estimation using Kalman filtering
Automotive engineering | 2002
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