A Vehicle Rollover Evaluation System Based on Enabling State and Parameter Estimation

Abstract

There is an increasing awareness of the need to reduce the traffic accidents and fatality rates due to vehicle rollover incidents. The accurate detection of impending rollover is necessary to effectively implement vehicle rollover prevention. To this end, a real-time rollover index and a rollover tendency evaluation system are needed. These should give high accuracy and be of a low application cost. In this article, we propose a rollover evaluation system taking lateral load transfer ratio (LTR) as the rollover index with inertial measurement unit as the system input. A nonlinear suspension model and a rolling plane vehicle model are established for the state and parameter estimation. An adaptive extended Kalman filter is utilized to estimate the roll angle and rate, which adjusts noise covariance matrices to accommodate the nonlinear model characteristic and the unknown noise characteristic. In the meantime, the forgetting factor recursive least squares method is utilized to identify the height of the center of gravity. The Butterworth filter is used to filter out the high-frequency noise of the acceleration signal and the index of LTR is accordingly calculated based on the estimation results. The proposed scheme is verified and compared through hardware-in-loop tests. The results show that the developed scheme performs well in a variety of operating conditions.