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The optimum design of suspension system is a subject with great importance as suspension system has a significant role in ride and handling of vehicles. Practical vehicle systems often contain some uncertainties, which should be considered in the optimum design process of suspension. In this study, a quarter-car model is used to investigate and optimize the dynamic response of cars with random uncertainty which comes from the terrain profile and the variable system parameters. In order to achieve an optimum robust design against uncertainty in reality, a new robust design approach is proposed, which is a combination of a multi-objective genetic algorithm and the generalized polynomial chaos theory. Then Pareto optimum robust design is applied to the quarter-car model with three performances criteria, related to ride comfort, suspension travel and road holding. As a result, an optimum robust design point for vehicle model is obtained by using the polynomial chaos approach.

Interval inverse problems can be defined as problems to estimate input through given output, where the input and output are interval numbers. Many problems in engineering can be formulated as inverse problems like vehicle suspension design. Interval metrics, instead of deterministic metrics, are used for the suspension design of a vehicle vibration model with five degrees of freedom. The vibration properties of a vehicle vibration model are described by reasonable intervals and the suspension interval parameters are to be solved. A new interval inverse analysis method, which is a combination of Chebyshev inclusion function and optimization algorithm such as multi-island genetic algorithm, is presented and used for the suspension design of a vehicle vibration model with six conflicting objective functions. The interval design of suspension using such an interval inverse analysis method is shown and validated, and some useful conclusions are reached.

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.

The structure of a classic self-energizing synchronizer is presented, and a simulation model is developed for analyzing the synchronizer performance. The self-energizing synchronizer has a disk spring and several energizing teeth on the sleeve for increasing the shift force. Besides, the asymmetric arrangement of chamfer teeth is applied to increase the torque for rotating ring and shift gears smoothly. The parameterized model of the typical synchronizer is developed with ADAMS for studying the synchronizer performance. In order to truly reflect the reality, the teeth of the claw plate are connected to the gear ring through bushing force alone, and the stiffness coefficient are obtained through the analysis of finite element model. Based on the dynamic model, the behavior of synchronizer with asymmetric arrangement of chamfer teeth, and the energizing effect of stiffness of the disk spring are studied. The simulation results can be used to design the synchronizer.

Vehicle systems often operate with some degree of uncertainty. This study applies the Chebyshev interval method to model vehicle dynamic systems operating in the presence of interval parameters. A full vehicle model is used as the numerical model and the methodology is illustrated on the steering wheel angle pulse input test. In the numerical simulation, suspension stiffness coefficients and suspension damping coefficients are chosen as interval parameters and lateral acceleration and yaw rate are chosen to capture vehicle dynamic characteristics. System responses in time domain are validated against Monte Carlo simulations and against the scanning approach. Results indicate that the Chebyshev interval method is more efficient than Monte Carlo simulations. The results of scanning method are similar to the ones obtained with the Chebyshev interval method.