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The study of vehicle coupling state estimation accuracy especially in observer-based vehicle chassis control for improving road handling and ride comfort is a challenging task for vehicle industry under various driving conditions. Due to a large amount of life safety arising from vehicle roll behavior, how to precisely acquire vehicle roll state and rapidly provide for the vehicle control system are of great concern. Simultaneously, uncertainty is unavoidable for various aspects of a vehicle system, e.g., varying sprung mass, moment of inertia and position of the center of gravity. To deal with the above issues, a novel dual observer approach, which combines adaptive Unscented Kalman Filter (AUKF) and Takagi-Sugeno (T-S), is proposed in this paper. A full-car nonlinear model is first established to describe vehicle lateral and vertical coupling roll behavior under various road excitation.

The control parameter of the semi-active suspension system varies with road profile; therefore, in this study a new algorithm based on cuckoo search (CS) optimization method and road estimation was proposed to investigate the characteristics of the nonlinear parameters and at the same time improve the riding comfort. Based on this, a seven degree of freedom full vehicle model was developed with nonlinear damper and spring. The sprung mass acceleration, pitch acceleration, and tire deflection could be selected as the objective functions, and the control current of semi active suspension was selected as optimization variable. A multi-object CS algorithm was utilized to obtain the optimal parameters under different road profiles, and a road estimation algorithm was used to identify the road level. Then the control parameters could be adjusted adaptively according to the level of the road.

The study of controllable suspension properties special in the characteristics of improving ride comfort and road handling is a challenging task for vehicle industry. Currently, since most suspension control requires the observation of unmeasurable state, how to accurately acquire the state of a suspension system attracts more attention. To solve this problem, a novel approach interacting multiple mode Kalman Filter (IMMKF) is proposed in this paper. Suspension system parameters are crucial for the performance of state observers. Uncertain suspension system parameters in various conditions, e.g. due to additional load, have significant effect on state estimation. Simultaneously, state transition among different models may be happened on the condition of varying system parameters.

The study of mechanical properties special in the characteristics of elastic element is a challenging task for vehicle industry. Since torsion bar spring acts as an important part of elastic element, and improves performance of torsion bar spring is of great concern. The effects of the torsion bar spring pre-setting precision on the presetting performance are presented. Based on elastic-plastic theories, the algebraic model of torsion bar spring is established to analyze the stress, torque and residual stress under the yield and plastic conditions in pre-setting process. Then, the stress and strain states of various torsion bar springs in different conditions are simulated using the validated finite element model in ABAQUS software. The simulation results show the effects of torsion error on the pre-setting performance are less than 5% in the pre-setting process.

Closed-loop coupling model, based on complex eigenvalue analysis, is one of the most popular and effective methods for brake squeal analysis. In the model, imaginary coupling springs are used to represent the normal contacting force between coupled nodes. Unfortunately, the physical meaning of these coupling springs was seldom discussed and there’s no systematic method to determine the value of spring stiffness. Realizing this problem, this paper, based on finite element model and modal synthesis technique, develops a new closed-loop coupling disc brake squeal model without introducing imaginary coupling springs. Different from the traditional model where two nodes at coupling interface are connected through a spring, these node-pairs in the new model are assumed to remain in tight contact during vibration. Details of the model, including force analysis, coordinate reduction and transformation and complex eigenvalue decomposition are given in this paper.

High fidelity mathematical vehicle models that can accurately capture the dynamics of car suspension system are critical in vehicle dynamics studies. System identification techniques can be employed to determine model type, order and parameters. Such techniques are well developed and usually used on linear models. Unfortunately, shock absorbers have nonlinear characteristics that are non-negligible, especially with regard the vehicle's vertical dynamics. In order to effectively employ system identification techniques on a vehicle, a nonlinear mathematical shock absorber model must be developed and then coupled to the linear vehicle model. Such an approach addresses the nonlinear nature of the shock absorber for system identification purposes. This paper presents an approach to integrate the nonlinear shock absorber model into the vehicle model for system identification.

This presentation evaluates the contribution of multi-objective programming scheme for the conceptual design of the Hybrid Electric Vehicle frame's structure using topological optimization. The compromise programming method was applied to describe the statically loaded multicompliance topology optimization. Solid Isotropic Material with Penalization (SIMP) was used as the interpolation scheme to indicate the dependence of material modulus upon regularized element densities. The sequential convex programming approach was applied to solve the optimization problem. The application on the chassis frame was used to demonstrate the characteristics of the presented methodologies based on the commercial software package OptiStruct.