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The optimization of vehicle suspension kinematic/compliance characteristics is of significant importance in the chassis development. Practical suspension system contains many uncertainties which may result from poorly known or variable parameters or from uncertain inputs. However, in most suspension optimization processes these uncertainties are not accounted for. This study explores the use of Chebyshev polynomials to model complex nonlinear suspension systems with interval uncertainties. In the suspension model, several kinematic and compliance characteristics are considered as objectives to be optimized. Suspension bushing characteristics are considered as design variables as well as uncertain parameters. A high-order response surface model using the zeros of Chebyshev polynomials as sampling points is established to approximate the suspension kinematic/compliance model.

A simulation model of the single cone synchronizer is presented using the dynamic implicit algorithm with commercial Finite Element Analysis (FEA) software Abaqus. The meshing components include sleeve gear, blocking ring and clutch gear, which are all considered as deformation body. The processes mainly contain the contact between sleeve teeth and blocking teeth, meshing period and the impact of sleeve teeth and clutch gear teeth, and these nonlinear contact steps are realized with Abaqus. In addition, a shift force derives from experiment is applied to the sleeve ring, and a moment is added to the clutch gear to realize the relative rotational speed. Based on the FEA model, the effect of the varied frictional coefficients between the cone surfaces of blocking ring and clutch gear on the synchronizer time and contact stress is discussed. Variation of stresses and contact force with respect to time are evaluated from this analysis.

A new recursive method is presented for real-time estimating the inertia parameters of a vehicle using the well-known Two-Degree-of- Freedom (2DOF) bicycle car model. The parameter estimation is built on the framework of polynomial chaos theory and maximum likelihood estimation. Then the most likely value of both the mass and yaw mass moment of inertia can be obtained based on the numerical simulations of yaw velocity by Newton method. To improve the estimation accuracy, the Newton method is modified by employing the acceptance probability to escape from the local minima during the estimation process. The results of the simulation study suggest that the proposed method can provide quick convergence speed and accurate outputs together with less sensitivity to tuning the initial values of the unidentified parameters.

Practical vehicle contains many uncertainties which may result from poorly known or variable parameters or from uncertain inputs. These uncertainties can be presented by fuzzy parameters, random parameters or interval parameters. A new uncertain analysis method is applied to the case in which the vehicle system contains both random parameters and interval parameters. This new uncertain method is a systematic integration of the Polynomial Chaos (PC) theory which accounts for random uncertainty and Chebyshev inclusion function theory which accounts for interval uncertainty. A multi-body vehicle model with both random parameters and interval parameters is used as a numerical model and vehicle handling is investigated in details. The Monte Carlo method combined with the scanning method is used to demonstrate the effectiveness of the proposed method for vehicle handling.