The Vertical Force Estimation Algorithm Based on Smart Tire Technology

Abstract

The real-time monitoring of the vertical force of the tire is the basis for ensuring the driving safety, handling stability, fuel economy and the riding comfort of the vehicle. An algorithm for the vertical force of the tire estimation based on the combination of intelligent tire technology and neural network theory is herein proposed. Firstly, the finite element model of the 205/55/R16 radial tire was established by ABAQUS and the validity of the finite element model was verified through static experiment and dynamic experiment. Secondly, the effects of inflation pressure, speed, load and tread wear on the tire contact patch length and the radial displacement at the virtual acceleration sensor were analyzed based on the finite element analysis method and control variable method. Finally, three kinds of the vertical force prediction algorithms based on the GA-BP neural network algorithm were established, and the network performance of each prediction model was tested. The results show that the vertical force prediction model with inflation pressure and the peak value of radial displacement as the characteristic input parameters has the highest prediction accuracy and the shortest calculation time. At the same time, the mathematical formula of the Model 3 was built.