Study Of Physical Parameter Identification Based On Vehicle Modal Characteristic Analysis And Its Application

Vehicle physical parameter identification is of great importance to vehicle dynamics research. Vehicle physical parameter identification, which is used to identify physical parameters of the vehicle model, is essential to vehicle dynamics analysis. Current vehicle physical parameter identification methodologies generally assume that some of vehicle physical parameters are known, and most of these methodologies need to measure both the excitation and response of vehicle at the same time, which strictly limits their applications. In order to overcome this limitation, vehicle physical parameter identification methods based on modal characteristic analysis are presented in this dissertation. Firstly, the State Variable Method(SVM) is applied to identify vehicle modal parameters according to the free decay response of vehicle. Secondly, least squares method(LSM) is adopted to estimate all of vehicle physical parameters. Vehicle physical parameter identification based on modal characteristic analysis is systematically investigated in this dissertation. Research works of this dissertation are arranged as follow:(1) Based on the modal shape characteristic of a 7 degree-of-freedom(7-DOF) vehicle model, different types of tire impulses excitation are designed to obtain the vehicle responses of different main vibration modes with high signal-to-noise ratio(SNR), which is the data basis of improving the accuracy of vehicle modal identification. The motion mode energy method is applied to analyze composite motion modes of vehicle responses under different tire excitations(impulse excitations,stochastic excitation), which provides a theoretical basis for the following research.(2) According to the vehicle responses of pre-designed impulse excitations, modal parameters of vehicle are identified via Fast Fourier Transform(FFT), Ibrahim Time Domain(ITD), Sparse Time Domain(STD) and SVM, respectively. The influences of the position inaccuracies of measured points and the asynchronism of tire impulse excitations on the errors of modal parameter identification are also discussed. Calculating the autocorrelation of vehicle body responses can help enhance the week signal characteristic of the vehicle body dominated roll vibration-mode, so that the natural frequencies of bodydominated bounce, pitch and roll vibration-modes are identified by using SSI.(3) Based on the modal parameter identified by SVM, two kinds of vehicle parameter identification methods are presented. The mass, damping and stiffness matrices of vehicle model are calculated by using the ratio between elements of the state matrix and added known masses, respectively. Then, the physical parameters of 7-DOF vehicle model can be estimated by using LSM. The effects of various cases, including the measurement errors of the increased mass, the measurement error of the vehicle structure parameters and the noise of the vehicle response, on the identification results are further discusses via numerical simulations. The analysis demonstrates that the proposed methodology is able to accurately estimate the physical parameters of 7-DOF vehicle model.(4) The experimental verifications of the proposed modal parameter identification methodology and physical parameter identification methodology are carried out by the experiments of an SUV and a school bus, respectively. The results demonstrate the effectiveness of the proposed methodologies. Meanwhile, the corresponding complete identification flow of the modal parameters and physical parameters is proposed and it is available to engineering.To sum up, the vehicle physical parameter identification methodology presented in this dissertation is to identify the modal parameters and physical parameters according to acceleration response of vehicle and can achieve all the physical parameters without any known parameters through experiment. The study is begun with vehicle dynamics analysis, and the various impulse excitation experiments are designed according to vehicle modal characteristic analysis, the response signal of vehicle stimulated by the aforementioned excitation is of high SNR, which can be used in the vehicle parameter identification. In sum, the accuracy of identification is improved and this dissertation provides a novel resolution for the vehicle parameter identification. The works in this dissertation is valuable in both theoretical research and engineering application.