Estimate Lateral Tire Force Based on Yaw Moment without Using Tire Model

This paper demonstrates the implementation of a model-based vehicle estimator, which can be used for lateral tire force estimation without using any highly nonlinear tire-road friction models. The lateral tire force estimation scheme has been designed, and it consists of the following three steps: the yaw moment estimation based on a disturbance observer, the sum of the lateral tire force of two front tires and two rear tires estimation based on a least-square method, and individual lateral tire force estimation based on a heuristic method. The proposed estimator is evaluated under two typical driving conditions and the estimation values are compared with simulator data from CarSim and experimental data provided by GM. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry. 1. Introduction To improve the handling performance and the safety of vehicles, a considerable number of active control systems for the vehicle lateral dynamics have commercially been developed and utilized over the last two decades. Lateral tire-road friction force is a vital signal that affects the stability of a vehicle under cornering. The accurate information of this force signal can greatly enhance the performance of some steering systems and active safety systems, such as electronic stability program (ESP). However, no commercial vehicles are equipped with sensors which can directly measure this force signal, which is due to either cost pressure or technical difficulty. This provides a room for an appropriate estimation algorithm. In fact, the ever-increasing demand for safety and driving comfortability makes it a very active research field in both academic society and auto industry. A vast variety of research results can be found in the literature [1–4]. However, tire-road friction is a very complex physical phenomenon, which is represented by various complicated mathematical models, such as Magic Formula, Fila model, and Dugoff tire model [5]. To utilize this kind of models, an online identification algorithm should be developed to detect the change of those parameters that classify road conditions. Even after considerable simplification, those models are still highly nonlinear, such as LuGre model [6], which makes the design of the controller or estimator extremely challenging. Moreover, the coupling between longitudinal and lateral tire forces further complicated the design process and lowered the robustness of the control and estimation algorithm. In this paper, we present a lateral tire force