A Hybrid Physical and Data-Driven Framework for Improving Tire Force Calculation Accuracy

The accuracy of tire forces directly affects the vehicle dynamics model precision and determines the ability of the model to develop the simulation platform or design the control strategy. In the high slip angle, due to the complex interactions at tire-road interfaces, the forces generated by the tires are high nonlinearity and uncertainty, which pose issues in calculating tire force accurately. This paper presents a hybrid physical and data-driven tire force calculation framework, which can satisfy the high nonlinearity and uncertainty condition, improve the model accuracy and effectively leverage prior knowledge of physical laws. The parameter identification for the physical tire model and the data-based compensation for the unknown errors between the physical tire model and actual tire force data are contained in this framework. First, the parameters in the selected combined-slip Burckhardt tire model are identified by the nonlinear least square method with tire test data. Then, the unknown nonlinear errors between the physical model and actual tire force are compensated by a neural network using the large data sets from indoor tire test facilities, including pure- and combined-slip conditions. Finally, the tire force calculation comparison illustrates that the proposed hybrid physical and data-driven tire force calculation framework can effectively improve the model accuracy, reflect the tire characteristics more accurately, adapt to different conditions, and is suitable for establishing a vehicle dynamics model.