Title :
An Improved Vehicle Rollover Prediction Algorithm
Author :
Xiaoping Shi ; Juan Bao
Author_Institution :
Sch. of Electr. & Inf. Eng., Hubei Univ. of Automotive Technol., Shiyan, China
Abstract :
The extended 3-DOF (degree-of-freedom) nonlinear rollover prediction model for heavy-duty vehicles is presented by considering the nonlinear characteristic of tire. The lateral force of rear wheels is revised with lateral acceleration of center of gravity, aiming at reflecting the impact of vehicle cornering condition. The parameters of current vehicle status are obtained in real time by using extended Kalman filter technique, and the vehicle roll state is estimated based on lateral load transfer ratio (LTR) algorithm. Simulation results show that the extended model is correct and more effective in predicting the current vehicle status and rollover tendency. In addition, the prediction results of our model are much closer to the practical data obtained in vehicle tests.
Keywords :
Kalman filters; acceleration; force; mechanical engineering computing; road vehicles; tyres; vehicle dynamics; LTR algorithm; center-of-gravity lateral acceleration; degrees-of-freedom; extended 3-DOF nonlinear rollover prediction model; extended Kalman filter technique; heavy-duty vehicles; improved vehicle rollover prediction algorithm; lateral load transfer ratio algorithm; rear wheels lateral force; rollover tendency; tire nonlinear characteristics; vehicle cornering condition; vehicle roll state estimation; vehicle tests; Data models; Force; Mathematical model; Predictive models; Tires; Vehicles; Wheels; extended rollover prediction model; nonlinear tire model; rollover prediction algorithm;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on
Conference_Location :
Shiyang
DOI :
10.1109/ICCIS.2013.301
