Research On Key Technologies Of Indirect Tire Pressure Monitoring System Based On Frequency Estimation

As the automobile consumption market increasingly develops, consumers extraordinarily care the safety performance of vehicles. 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 nonuniform 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 lucubrated, the research scope 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 introduced; and spring-damper model has been established according to the subject's task.(2) The characteristic of wheel speed sensor, the measurement and error source of wheel speed are investigated. How to reject the singular point has been described. Methods of attenuating wheel speed sensor errors and error matching based on Resilient Back Propagation Neural Network have been presented. The corresponding neural network has been designed and the wheel speed correction has been realized finally.(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 nonuniform sampling signal in allusion to its characteristic. Firstly, Reconstruction process is transformed into solving Toeplitz formulation; then new Toeplitz matrix has been obtained by adding adaptive weights and resolved by utilizing preconditioned conjugate gradient method ultimately. Simulation results show that AWPCG algorithm can reconstruct nonuniform sampling signal precisely without time delay.(4) Ways to estimate the resonance frequency have been discussed, mainly consisting of non-parametric and parametric spectral estimation. Auto-recursive (AR) model parametric spectral estimation is chosen by comparing their advantages and disadvantages. How to establish AR model and estimate model parameters have been analyzed, several means for parameters estimation have been compared and the resonance frequency has been computed by using estimated AR model parameters.(5) Algorithms for wheel speed sensor errors' elimination and match through Resilient BP Neural Network by using wheel speed signal under different pressures, nonuniform sampling signal' reconstruction by AWPCG and resonance frequency estimation by AR model parametric way have been validated. The results show that the algorithms proposed in the paper are effective and feasible.The researches on the algorithms mention above in TPMS have reference values and practical meanings for developing and perfecting vehicle safety further.