As global warming and environmental problems are becoming more serious, tires are required to accomplish balanced performance such as low rolling resistance, wet braking performance, driving stability, and ride comfort, while minimizing wear, noise, and weight.
Furthermore, to respond to the rapid changes in the market environment, there is a strong demand for tire performance prediction and testing methods adapted to MBD. In MBD, Magic Formula and Ftire are used in performance design to predict handling and stability and ride comfort. However, predicting tire wear life, which is affected by both vehicle and tire characteristics, is difficult. And practical prediction method for estimating tire wear life has long been awaited.
So, we had proposed an experimental-based tire wear life prediction method that using actual measurement tire characteristics and an abrasive wear volume formula. This method achieves accuracy that can be practically used in the early stages of vehicle development without the need for time-consuming and expensive real vehicle tests.
However, due to the electrification of vehicles and new legal regulations such as pass-by noise and dust, the accuracy of existing method has become insufficient.
To address this technical challenge, in this paper, vehicle characteristic values such as toe angle and camber angle, which affect tire life, are newly introduced to the method. In addition, since the contribution of each parameter varies depending on the tire mounting position and tire contact patch ribs, a method to optimize the weighting of each parameter has been introduced using machine learning technique from the result of actual tire wear tests and various tire characteristics, and vehicle characteristics values.