Identification Research Of Risk State On Truck Braking System Based On Braking Distance

Truck transportation plays an important role in our country economy, it activate the research of problem of truck safety, expecially when the massive traffic accident occurred. The braking performance of truck is the key factor to ensure safety of truck. Proceeding from the braking systemof truck, this paper has proposed an identification method of risk state based on braking distance.After the main analysis of the effect of braking distance by the braking pipe pressure, wear and temperature, the effect factors can be divided into three areas, i.e., parameters of the vehicle braking system, vehicle condition factors and road environment factors, the effect system of braking distance has been given,then modeling the braking distance according to the six factors. A truck CA1169PK2L2EA80 which produced by FAW, has been used to as the test truck, we have done the neutral coasting test, idel coasting test,and braking test, to measure the deceleration which caused by resistance, then calibrated the the braking distance model, and modified the model when braking force is greater than the adhesion force by using the braking test results.The simulation of braking performance is carried out with simulation software Tesis_veDYNA, parametric modeling to simulate the test truck, used the result of braking test to verify the simulation accuracy. The simulation accuracy was reliable, so we can use it to do the simulation when the truck in the risk state.According to the result to quantitative analysis of the influence of braking efficiency under single factor.The orthogonal test method was used to design a multi-parameters simulation test, the major-minor factor order which influence the braking distance can be determinded by processing the simulation results. The model when braking force is less than the adhesion force has been modified.The braking distance model and threshold requirement were the condition to determine the threshold of single parameter and multi-parameters of braking system, the threshold curves and surfaces have been given. Proposed the definition of risk level, and two risk level calculation models have been designed, they are customize model and the BP neural network model, it can be selected according to the actual status of application. Risk level is used to identify risk status of brake system. The forewarning process has been presented at last.