To get the certain response of vehicle during the driving process, it’s necessary to measure the road irregularities. Existing method of gauging the roughness is based on physical measurements and the instrument is installed under the vehicle, which is expensive and will affect the vehicle dynamic responses. This paper shows an easier method to estimate the road roughness by measuring and calculating the power spectral density (PSD) of unsprung mass accelerations. This approach is possible due to the relationship between these two via a transfer function. By comparing the power spectral densities of estimated road and the standard classes, we can classify the current road classes easily. Besides, this paper also shows that it’s feasible to estimate the road profile by calculating the PSD of unsprung mass accelerations directly.
This paper presents a novel approach for improving the estimation accuracy of the road profile for a vehicle suspension system. To meet the requirements of road profile estimation for road management and reproduction of system excitation, previous studies can be divided into data-driven and model based approaches. These studies mainly focused on road profile estimation while seldom considering the uncertainty of parameters. However, uncertainty is unavoidable for various aspects of suspension system, e.g., varying sprung mass, damper and tire nonlinear performance. In this study, to improve the estimation accuracy for a varying sprung mass, a novel algorithm was derived based on the Minimum Model Error (MME) criterion and a Kalman Filter (KF). Since the MME criterion method utilizes the minimum value principle to solve the model error based on a model error function, the MME criterion can effectively deal with the estimation error. Then, the proposed algorithm was applied to a 2 degree-of-freedom (DOF) suspension system model under ISO Level-B, ISO Level-C and ISO Level-D road excitations. Simulation results and experimental data obtained using a quarter-vehicle test rig revealed that the proposed approach achieves higher road estimation accuracy compared to traditional KF methods.
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