An Investigation on the Data Mining to Develop Smart Tire

摘要

A smart tire is required to improve driving safety for an intelligent vehicle especially for automated driving electric vehicles. It is necessary to provide information of tire contact forces (vertical, longitudinal, and lateral directions) to control velocity and steering angle of the autonomous vehicle so as to ensure driving stability. This study presents a smart tire system with the data mining to estimate the vertical load by using the tire deformation data in particular. Firstly, the hardware system construction of the smart tire in which tire deformation on driving by using strain gauge is described. And then the test condition is set up and total 27 sets of experimental data are processed to perform correlation analysis for specifications of measured waves. Next, the estimation algorithm of smart tire vertical load is derived by considering the area of tire-ground contact patch and also by introducing compensate coefficient of transverse direction length of contact area. The experimental results show the proposed estimation algorithm is feasible and precise. The advanced adaptive and precise estimation algorithm with artificial neural network will be developed further.