Search Results

On-center feel is a multivariate problem that a performance is represented using put-together several sub-characteristics such as torque feedback, response, torque linearity, hysteresis, returnability, etc. For the improvement of a multivariate problem, multi objective optimization should be carried out. However each characteristic which ignores correlation between characteristics is usually optimized up to now. The objective of this research, Mahalanobis Taguchi System (MTS) technique is grafted to on-center steering feel to obtain the efficient improvement. MTS technique can optimize the unified on-center index which is generated in consideration of correlation between characteristics. In this research, first an effective value of MTS technique is verified with on-center steering feel which has the multivariate characteristic. Second, on-center steering feel is improved using MTS technique and Design of Experiments (DOE).

Elastokinematic properties of the suspension system are one of major factors that improve ride and handling performances. Their properties have been quasi-statically determined using the in-door measurement device, such as the SPMD (Suspension Parameter Measurement Device). The elastokinematic properties in driving conditions, called dynamic compliance characteristics, are defined as the superposition of quasi-static properties, called static compliance characteristics. However, this superposition method has difficulties in predicting the suspension non-linearities under high lateral acceleration and transient behaviors. In this paper, dynamic compliance characteristics are directly determined through on-board measurements and transformation matrix. Their applications in validating ADAMS full vehicle model are discussed. Some experimental and simulation results about their unique properties and relationships with SPMD are presented.

A determination of the vehicle states and tire forces is critical to the stability of vehicle dynamic behavior and to designing automotive control systems. Researchers have studied estimation methods for the vehicle state vectors and tire forces. However, the accuracy of the estimation methods is closely related to the employed model. In this paper, tire lag dynamics is introduced in the model. Also application of estimation methods in order to improve the model accuracy is presented. The model is developed by using the global searching algorithm, a Genetic Algorithm, so that the model can be used in the nonlinear range. The extended Kalman filter and sliding mode observer theory are applied to estimate the vehicle state vectors and tire forces. The obtained results are compared with measurements and the outputs from the ADAMS full vehicle model. [15]

Computer simulations are popular for modeling vehicle system dynamics. However, further refinement of the vehicle dynamic model is required for extensive use in the automotive industry. In this paper, the model refining procedure is illustrated by developing reliable kinematic models verified with laboratory test results; instrument test data; and a mathematical optimization method. More specifically, simple kinematic models are developed for reduced computation times using ADAMS. They are tuned by the gradient-based optimization technique using the results from a laboratory testing facility, which includes the compliance effect in order to use the kinematic models in dynamic simulations. Also the Magic Formula tire model is developed using the optimization method and tire property data for the STI (Systems Technology, Incorporated) tire model.

In the early stage of vehicle development process, it is customary to establish a set of goals for each kinematic and compliance (K&C) characteristic and try to find out design variables such as the location of hard points and bushing stiffness which can achieve these goals. However, since it is very difficult to find out adequate set of design variables which satisfy all the goals, many engineers should rely on their own experiences and intuitions, or repeat trial and error to design a new suspension and improve old one. In this research, we develop a suspension design process by which suspension K&C characteristic targets can be achieved systemically and automatically. For this purpose, design optimization schemes such as design of experiments (DoE) and gradient-based local optimization algorithm are adopted.

This paper presents a methodology of correlation between subjective and objective measures of vehicle on-center handling performance. The subjective measure is a professional test driver's rating of vehicle handling, while the objective measure assesses the handling performance via vehicle dynamic responses. Vehicle test data obtained from field testing has been analyzed to investigate links between the objective and subjective measures. Fifty-six physical parameters have been derived from on-centering hysteresis curves. Statistical tools are employed to obtain good correlation between driver rating and physical parameters. Using an interaction formula, a statistical model which relates the driver rating and principal physical parameters has been obtained. The proposed methodology will be used to show the physical parameters influence on subjective assessment and even to predict the subjective assessment of a vehicle handling performance.

In the view point of driving safety, the crosswind sensitivity of a vehicle becomes more important, as the driving speed in highway gets higher in these days. The sensitivity of a vehicle to crosswind depends on many factors, including the design of the suspension and aerodynamics of the body, etc. However, the knowledge about this phenomenon has still to be improved, in order to develop vehicle with optimum characteristics for crosswind stability. In this research, the physics behind the sensitivity of a vehicle is discussed in detail through various kinds of virtual test using computer aided engineering (CAE) simulation scheme. In the first, a reliable simulation model for vehicle, driver, wind generator and interactions among them is built. This simulation model is verified by comparison with test results of real vehicle. Then, the sensitivity analysis is carried out to find out the most influential design parameters.