Integrated control systems for vehicle-handling stability are usually based on the steering bifurcation mechanism. The best integrated control performance is obtained by coordinating different control methods. However, in vehicle steering and driving conditions, the coupling characteristics of the longitudinal forces and the lateral forces of the tyres must lead to changes in the bifurcation characteristics. The corresponding vehicle dynamics stability region has to be redetermined. The corresponding integrated control method also needs to be adjusted. Therefore, in combination with the physical significance of the dynamics equilibrium point of the vehicle system, the definition of the driving stability region of the vehicle based on the characteristics of the driving torque and the steering angle bifurcation is proposed. With the concept presented above, the five-degree-of-freedom non-linear vehicle system model for the driving stability region of the vehicle was solved. The simulation results show that, according to the driving stability region of the vehicle, the vehicle dynamics stability with different driving torque inputs and different front-wheel steering-angle inputs can be accurately estimated. The study of the driving stability region of the vehicle is beneficial for engineering applications in non-linear automotive dynamics research. In addition, it provides the theoretical basis for integrated control of the vehicle-handling stability.

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Many researches on vehicle planar motion stability focus on two degrees of freedom(2DOF) vehicle model, and only the lateral velocity (or side slip angle) and yaw rate are considered as the state variables. The stability analysis methods, such as phase plane analysis, equilibriums analysis and bifurcation analysis, are all used to draw many classical conclusions. It is concluded from these researches that unbounded growth of the vehicle motion during unstable operation is untrue in reality thus one limitation of the 2DOF model. The fundamental assumption of the 2DOF model is that the longitudinal velocity is treated as a constant, but this is intrinsically incorrect. When tyres work in extremely nonlinear region, the coupling between the vehicle longitudinal and lateral motion becomes significant. For the purpose of solving the above problem, the effect of vehicle longitudinal velocity on the stability of the vehicle planar motion when tyres work in extremely nonlinear region is investigated. To this end, a 3DOF model which introducing the vehicular longitudinal dynamics is proposed and the 3D phase space portrait method is employed for visualization of vehicle dynamics. Through the comparisons of the 2DOF and 3DOF models, it is discovered that the vehicle longitudinal velocity greatly affects the vehicle planar motion, and the vehicle dynamics represented in phase space portrait are fundamentally different from that of the 2DOF model. The vehicle planar motion with different front wheel steering angles is further represented by the corresponding vehicle route, yaw rate and yaw angle. These research results enhance the understanding of the stability of the vehicle system particularly during nonlinear region, and provide the insight into analyzing the attractive region and designing the vehicle stability controller, which will be the topics of future works.

Vehicle mass is a critical parameter for economic cruise control. With the development of active control, vehicle mass estimation in real-time situations is becoming notably important. Normal state estimators regard system error as white noise, but many sources of error, such as the accuracy of measured parameters, environment and vehicle motion state, cause system error to become colored noise. This paper presents a mass estimation method that considers system error as colored noise. The system error is considered an unknown parameter that must be estimated. The recursive least squares algorithm with two unknown parameters is used to estimate both vehicle mass and system error. The system error of longitudinal dynamics is analyzed in both qualitative and quantitative aspects. The road tests indicate that the percentage of mass error is 16%, and, if the system error is considered, the percentage of mass error is 7.2%. The precision of mass estimation improves by 8.8%. The accuracy and stability of mass estimation obviously improves with the consideration of system error. The proposed model can offer online mass estimation for intelligent vehicle, especially for heavy-duty vehicle (HDV).

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