Research On Key Technologies Of Tire Pressure Monitoring System By Indirect Frenquency Method

As the automobile consumption market develops increasingly, consumers care the safety performance of vehicles extraordinarily. The Tire Pressure Monitoring System (TPMS) which is mainly used to monitor the tire pressure during driving in real-time is an effective measure for guaranteeing safe driving. TPMS has crucial significance for improving the safety of vehicles, so it has wide application foreground. The implementation process of indirect TPMS based on frequency estimation includes a series of key technologies: the error process of wheel speed signal which involves singular point rejecting, attenuating toothed wheel errors and errors match; reconstruction of non-uniform sampling signal, which means how to effectively reconstruct the original signal without distortion; resonance frequency estimation and so on.The key technologies mentioned above have been investigated, and the main creative achievements include:(1) The tire pressure's impact on tire has been analyzed; the continuous time tire model and discrete time torsional vibration model have been established by using the spring-damper model of vertical and torsional direction; the function relation between resonance frequency and model parameters has been deduced.(2) The characteristic of wheel speed sensor, the measurement and error source of wheel speed were investigated. How to reject the singular point has been described. Method of attenuating wheel speed sensor errors and error matching based on Resilient Back Propagation Neural Network has been presented. The corresponding neural network has been designed and the wheel speed correction has been realized finally. The results of simulation and experiment show that the method can make the errors reduce to 2×10-4, which attenuates sensor errors effectively and improves the accuracy greatly.(3) Means for signal reconstruction have been surveyed comparatively, comprising interpolation, basis expansion and so on. AWPCG (Adaptive Weights Preconditioned Conjugate Gradient method) has been presented to reconstruct the non-uniform sampling signal in allusion to its characteristic. Firstly, Reconstruction process is transformed into solving Toeplitz formulation; then new Toeplitz matrix can be obtained by adding adaptive weights and resolved by utilizing preconditioned conjugate gradient method ultimately. Simulation results show that AWPCG algorithm can reconstruct non-uniform sampling signal precisely without time delay and that the absolute error is not larger than 0.5rad/s and the relative error is less than 0.01.(4) To lessen the complexity of the non-uniform sampling interpolation algorithm and shorten the spend time of the interpolation process, this dissertation presents the ways to estimate the resonance frequency by means of dealing with the adjusted data but not interpolated using non-uniform wavelet transform. The simulation results show that the estimated resolution utilizing interpolated data and uniform sampling wavelet transform is higher than that utilizing not interpolated data and non-uniform sampling wavelet transform, while the latter has the obvious benefit on saving dealing time and system memory resource.Algorithms for wheel speed sensor errors'elimination and match through Resilient BP Neural Network by using wheel speed signal under different pressures, non-uniform sampling signal'reconstruction by AWPCG and resonance frequency estimation by AR model parametric way and non-uniform sampling wavelet transform way have been validated. The results show that the algorithms proposed in the dissertation are effective and feasible.The researches on the algorithms mentioned above in TPMS have reference values and practical meanings for developing and perfecting vehicle safety further.