DETECTION OF VEHICLE TRIPPED AND UNTRIPPED ROLLOVERS BY A NOVEL INDEX WITH MASS-CENTER-POSITION ESTIMATIONS

摘要

This paper presents a novel mass-center-position (MCP) metric for vehicle rollover propensity detection. MCP is first determined by estimating the positions of the center of mass of one sprung mass and two unsprung masses with two switchable roll motion models, before and after tire lift-off. The roll motion information without saturation can then be provided through MCP continuously. Moreover, to detect completed rollover statues for both tripped and untripped rollovers, the criteria are derived from d'Alembert principle and moment balance conditions based on MCP. In addition to tire lift-off, three new rollover statues, rollover threshold, rollover occurrence, and vehicle jumping into air can be all identified by the proposed criteria. Compared with an existing rollover index, lateral load transfer ratio, the fishhook maneuver simulation results in CarSim® for an E-class SUV show that MCP metric can successfully predict the vehicle impending rollover without saturation for untripped rollovers. Tripped rollovers caused by a triangle road bump are also successfully detected in the simulation. Thus, MCP metric can be successfully applied for rollover propensity prediction.