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Ride comfort is simply defined as the vibration performance of the vehicle which is excited by road surface roughness, generally as the vehicle moves at specific constant velocity over the road profile. Ride comfort was an important index for heavy truck, due to long distance transfer and long time driving. In order to improve the ride comfort of a heavy truck, a detailed model, including flex frame, chassis suspension, cab suspension, powertrain, etc., was built and assembled by MSC.ADAMS software. Simulation and testing data were consistent very well, which showed the correctness of the model. The optimization of chassis and cab suspension including the stiffness of the leafspring, the damping of the shock absorber, etc. was carried out to improve the ride comfort of the vehicle. The ride comfort testing was carried out on the proving ground to verify the effectiveness the optimization results. The testing results shows that the ride comfort has been improved after tuning.

Transmission gear whine noise is one of the main noise problems in powertrain NVH, which is caused by dynamic meshing force of gear pairs, it acts as transmission error. Due to the coupling effects of transmission gears, shaft, bearings and housing, it needs comprehensive management from many aspects to solve the problem of gear whine noise. Aiming at gear whine noise of a 4-speed AMT used in electric bus, the main noise sources is identified by using the order tracking analysis approach firstly. Secondly, gear misalignment and contributions of system deformation to the misalignment is analyzed by means of simulation tools, and the factor is taken into account in the subsequent gear modification design. At last, based on the improved Smith slice method, the calculation model of transmission error of helical gears is established.

This paper presents the relationship between suspension and steering systems and wheels, and proposes the vehicle dynamics modeling method. A vehicle dynamics model combined with the suspension K&C test data of a concrete vehicle was built based on the method. The simulation results show that the method is correct and feasible, and the dynamics model performed characteristics of the suspension and steering systems with high precision can be used for the followup simulation and optimization.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties.

To design and optimize the vehicle driveline is necessary to decrease the fuel consumption and improve dynamic performance. This paper describes a methodology to optimize the driveline design including the axle ratio, transmission shift points and transmission shift ratios considering uncertainty. A new and flexible tool for modeling multi-domain systems, Modelica, is used to carry out the modeling and analysis of a vehicle, and the multi-domain model is developed to determine the optimum design in terms of fuel economy, with determinability. Secondly, a robust optimization is carried out to find the optimum design considering uncertainty. The results indicate that the fuel economy and dynamic performance are improved greatly.