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In this paper, an approach to obtain an accurate yet simple model for vehicle handling is proposed. The approach involves linearizing a full-car MBD model and dividing the large-order system states into two groups depending on their effect on ride quality and handling performance. Then, the singular perturbation method is used to reduce the model size and to compensate for the steady state relationship between the two state groups. Based on simulation using ADAMS, the results show an accurate matching between the reduced model and the original MBD model. The benefits of the approach are illustrated by comparing step responses with the conventional nominal model. Also, the reduced-order model obtained by the proposed approach resulted in improved performances under sliding mode control even with smaller switching gains than what are required for the conventional bicycle model.

Vehicle dynamics are dominated by tire forces and their precursor input variables, and a few inertial and suspension properties. Tire forces have been studied since 1930's with the purpose of comprehending and improving vehicle control and handling. Although work has been done on modeling the tire forces using various forms of polynomial interpolation of experimental data, these properties are not thoroughly recognized. As vehicle dynamics' technology improves, the more accurate tire model is required. This paper presents a tire force model by the neural networks. The neural network tire model relates the tire force as a function of vertical load, slip angle, camber angle, and longitudinal force. Many published tire model papers described the side force as Fy(α,γ,Fz)=F(α,Fz)+F(γ,Fz). However, the neural network tire model described the side force as Fy=F(α,γ,Fz) which is closer to the true tire force behavior than the conventional tire model.