Institute of Fundamental Technological Research

Accurate information about vehicle parameters and road roughness is of great significance in vehicle dynamic analysis, road driving quality, etc. In this study, a method for estimating vehicle parameters and road roughness was developed using the measured vehicle responses from field tests which is efficient, economical, and accurate. First, the full-vehicle model was introduced. Then, vehicle modal parameters were identified using the consequent free responses of a vehicle passing over bumps. Second, the expression of the vehicle frequency response function (FRF) with respect to the wheel contact point was derived from the vehicle equation of motion, and a road roughness estimation method based on the vehicle FRF was developed. Third, field tests in which the vehicle passes over bumps were performed for vehicle model identification. Finally, field tests for road roughness estimation were carried out using a calibrated vehicle to verify the effectiveness of the proposed methods.

road roughness, vehicle parameters, modal identification, frequency response function (FRF), vehicle response

Accurate vehicle parameter information plays an important role in assessing the conditions of roads and bridges, along with the corresponding maintenance. This study considered a vehicle parameter identification method based on a vehicle frequency response function (FRF). First, the vehicle FRF was deduced with respect to the displacements of the vehicle-road contact points, thereby building the relationships among the FRF, vehicle responses, and road profile in the frequency domain. Next, using the responses of vehicles passing over on-road bumps of known size, a direct estimation of the vehicle FRF was described. Then, a combination of Tikhonov regularization and a shape function method was used to update the estimated vehicle FRF by removing the singular data owing to the direct computation of the vehicle FRF. Subsequently, the modifying factors of the vehicle parameters were iteratively identified based on a sensitivity analysis of the estimated FRF to the vehicle parameters. A numerical simulation for vehicle parameter identification was performed to test the effectiveness of the proposed methods, considering a 5% Gaussian noise pollution and the influences of different driving speeds. At last, field tests of a vehicle passing over bumps were performed for the verification of vehicle parameter identification

vehicle parameter identification, frequency response function, Tikhonov regularization, shape function method