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

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.

structural health monitoring, road roughness, vehicle response, frequency response function, Fourier transform