Tire-road friction coefficient estimation method design for intelligent tires equipped with PVDF piezoelectric film sensors

Intelligent tires, as an emerging technology, have great potential in real-time monitoring of tire-road contact information and new automotive active safety design. In this paper, a tire-road friction coefficient estimation method is proposed based on intelligent tire technology. First, an intelligent tire finite element model with the use of five PVDF piezoelectric film sensors attached to the inner liner of the tire is built using ABAQUS software, and the validity of the finite element model is verified by a piezoelectric intelligent tire test platform. Then, through the finite element analysis method and control variable method, the influence of sideslip angle on the voltage signal of each piezoelectric film under the rolling state of tire is analyzed, and when the sideslip angle is 4 degrees, the influence of load, tire pressure, vehicle speed and slip ratio on the voltage signal of each piezoelectric film under the rolling state of tire is analyzed. Finally, based on the signal response analysis of the piezoelectric film sensors, the response mechanism of the predicted object and the linear correlation analysis, the input eigenvalues of each prediction model are extracted. The longitudinal contact patch length estimation model based on the numerical method and the sideslip angle, vertical force, lateral force and aligning moment estimation model based on the neural network algorithm are built, and the predicted parameters are input into the brush tire model to solve the tire-road friction coefficient. The result shows that the estimation error percentage of the friction coefficient estimation method without genetic algorithm optimization is 16.98%, and the estimation error percentage of the friction coefficient estimation method with genetic algorithm optimization is 5.14%, indicating that the genetic algorithm optimization effect is ideal, and the friction coefficient estimation method is practical.

Automated summary

This paper proposes a tire-road friction coefficient estimation method based on intelligent tire technology. Through finite element analysis and control variable method, the influence of sideslip angle on the voltage signal of each piezoelectric film under the rolling state of tire is analyzed, and the influence of load, tire pressure, vehicle speed, and slip ratio on the voltage signal of each piezoelectric film is also analyzed. Based on signal response analysis, prediction models are built and input into the brush tire model to solve the tire-road friction coefficient. The result shows that the estimation error percentage with genetic algorithm optimization is 5.14%, indicating the practicality of the friction coefficient estimation method.