Multi-body modeling and dynamic analysis of the heavy-load multi-axle vehicle Based on 6S/6M ABS control

Abstract

The study investigates the braking characteristics of heavy-load five-axle special vehicle to improve the braking safety of it. The mechanical system with strong coupling is studied by means of multi-body dynamics analysis method, and the refined vehicle dynamics model including braking system, suspension system, steering system, and other components is established via platform of Adams/Car. Through road test experiments, the accuracy and reliability of the model under conditions of 80 m constant turning radius circle and emergency braking of 60 km/h initial speed are verified. The classical ABS (Anti-lock Braking System) control strategy with logic threshold including a road recognition module and a slip ratio calculation module is established in Simulink, which matches the ABS control system of 6 S/6 M for the five-axle special vehicle well. By changing adhesion conditions of a single road and the control channel modes of the split-mu road, the control effect of the ABS control system on the braking performance of the five-axle vehicle is determined by the co-simulation. The experimental and simulation results indicate the following: (1) The vehicle dynamics model can effectively simulate the structure of the actual vehicle mechanical system and mechanical constraints, and has good braking performance and steering performance; (2) ABS external control system and heavy-load five-axle vehicle model can be matched, and during the control process, logic thresholds need to be repeatedly adjusted to adapt to different types of roads. (3) The change of road adhesion coefficient will affect the control effect of ABS. The baking performance can be improved more in low adhesion road than in high adhesion road. (4) Split-mu road is more special and has a great impact on the lateral stability of braking. It can be seen through comparative analysis of high-selection and low-selection control that braking safety is better under low selection control mode.