Real-time monitoring of tire wear is a hot spot in the research of automobile tires, and it has a great significance to ensure the safety of automobile driving. A tire wear estimation algorithm was proposed based on the relevant knowledge of finite element modal analysis theory and the concept of intelligent tires in this paper. First, the finite element model of the 205/55/R16 radial tire was established through the ABAQUS software, then the finite element method was used to simulate and analyze the influence of tire inflation pressure, load, tire wear, and speed on the tire radial vibration frequency. The simulation results show that inflation pressure and tire wear shows an upward trend with the increase of the vibration frequency of each order in the tire radial direction, and load and speed increase with what increases of tire radial increase frequency. Based on simulation analysis data, combined with the relationship between tire inflation pressure, load, tire wear, speed, and radial vibration frequency, a neural network-based tire wear estimation algorithm is proposed. The estimate results show that the predicted wear curve and the actual wear curve have a higher degree of overlap, the average error is 0.0874 mm, and the average error percentage is 2.78%, Thus, a feasible tire wear estimation algorithm is proposed.

轮胎磨损的实时监测是汽车轮胎研究的热点，对保证汽车行驶的安全性具有重要意义。本文基于有限元模态分析理论的相关知识和智能轮胎的概念，提出了一种轮胎磨损估计算法。首先，通过ABAQUS软件建立205/55 / R16子午线轮胎的有限元模型，然后采用有限元方法来模拟和分析轮胎充气压力，载荷，轮胎磨损和速度对轮胎的影响。径向振动频率。仿真结果表明，充气压力和轮胎磨损随着轮胎径向各阶振动频率的增加而呈上升趋势，负载和速度随轮胎径向频率的增加而增加。基于仿真分析数据，结合轮胎充气压力，负荷，轮胎磨损，速度和径向振动频率之间的关系，提出了一种基于神经网络的轮胎磨损估计算法。估计结果表明，预测磨损曲线与实际磨损曲线具有较高的重叠度，平均误差为0.0874 mm，平均误差率为2.78％，提出了一种可行的轮胎磨损估计算法。