Two passenger cars were driven at several speeds over several road profiles to evaluate the subjective ratings of steering wheel vibration. A 3-axis translational accelerometer was mounted on the steering wheel to measure the acceleration signal transmitted to the hands. Correlations were determined between the measured accelerations and the subjective ratings of 4 expert drivers and 10 general drivers by using Stevens' power law. The subjective ratings were found to be more highly correlated with the r.m.q. (root mean quad) values of the frequency-weighted acceleration than the r.m.s. (root mean square) values of the frequency-weighted acceleration. Also, the maximum values of r.m.q. (i.e., the component values in the dominant axis) had the highest correlation with the subjective ratings.
An analytical vehicle model is essential for the development of vehicle design and performance. Various vehicle models have different complexities, assumptions and limitations depending on the type of vehicle analysis. An accurate full vehicle model is essential in representing the behaviour of the vehicle in order to estimate vehicle dynamic system performance such as ride comfort and handling. An experimental vehicle model is developed in this article, which employs experimental kinematic and compliance data measured between the wheel and chassis. From these data, a vehicle model, which includes dynamic effects due to vehicle geometry changes, has been developed. The experimental vehicle model was validated using an instrumented experimental vehicle and data such as a step change steering input. This article shows a process to develop and validate an experimental vehicle model to enhance the accuracy of handling performance, which comes from precise suspension model measured by experimental data of a vehicle. The experimental force data obtained from a suspension parameter measuring device are employed for a precise modelling of the steering and handling response. The steering system is modelled by a lumped model, with stiffness coefficients defined and identified by comparing steering stiffness obtained by the measured data. The outputs, specifically the yaw rate and lateral acceleration of the vehicle, are verified by experimental results.
A multi-body vehicle model is more accurate than a lumped-mass model; however, it usually requires a cumbersome process to obtain accurate data for compliant elements such as bushings. To avoid this complex process associated with bushings or kinematic linkages, a semiempirical vehicle model was developed by switching the suspension characteristics to measured kinematic and compliance data. In a semiempirical vehicle model, it is assumed that the inertia effects in the suspension subsystem are ignored by replacing components of the suspension system with massless links. Although this semiempirical vehicle model is simple, it sometimes degrades the accuracy in the dynamic responses compared with those of the multi-body vehicle model. Thus, it is necessary to include the dynamic characteristics of the suspension in an semiempirical vehicle model to increase its accuracy. In this paper, a new technique is proposed that considers the dynamic effect of the suspension system in an semiempirical vehicle model by using the Maxwell force model. By comparing the vertical acceleration with that obtained by the proposed method, the dynamic responses with the semiempirical vehicle model become much closer to those of the multi-body vehicle model. The results show that the force with respect to the suspension's motion has a strong effect on the dynamic responses, and this relationship becomes important to improve the accuracy of the semiempirical vehicle model.
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