Abstract: While the heavy duty vehicle has a high gravity-center, large loads and relatively narrow tread in comparison with the height of vehicle. Therefore the rollover threshold is low; consequently, rollover tends to occur. A real-time rollover prediction model is built, and off-line identification technology is utilized to identify the key parameters of 3-DOF vehicle model. In addition, by using the improved model to carry out on-line rollover prediction and control, thereby realizing the precision prediction of dynamic rollover threat. On this basis, Kalman filter technique is integrated into the improved time to rollover(TTR) algorithm, the lateral load transfer ratio is taken as the condition of rollover threshold, and on the basis of the current vehicle state, the degree of rollover threat in forthcoming three seconds is predicted, and TTR value is calculated in real time. Once the TTR value reaches rollover limit, the system will trigger the warning device automatically. A rollover warning test platform is used to verify the rollover prediction system. The field test results show that the heavy duty vehicle rollover warning system is very effective in prevention of rollover occurrence and attains the expected design object.

Key words: Improved time to rollover(TTR) algorithm, Heavy duty vehicle, Rollover warning system