New Method for Monitoring Tire Pressure of Cars Based on the Tire Radial Deformation

Monitoring tire pressure of cars and signaling abnormal conditions is an important means to prevent deadly accidents. Large achievements have been gained, especially in direct tire pressure monitoring system（TPMS）. But there has been rarely research on indirect TPMS in the world. In China, the research on indirect TPMS is almost lacking. The international research on the indirect monitoring tire pressure method is mainly based on measuring and comparing the rotating speed of wheels. But it is very difficult to measure wheel rotating speed accurately because of the influence of many random factors. In this paper, the authors propose a new method in which the tire pressure can be monitored indirectly. This method can be used for tire calibration, wheel speed frequency standardization, wheel speed frequency comparison, and abnormal tire pressure determination. The pulse frequencies from wheel speed sensors of ABS are used to indicate tire deformation. Because the frequency has a relationship with tire deformation, the tire deformation reflects the tire pressure. Small sample statistics is used in the new method to increase the accuracy, and the experimental samples using the principle of the new method have been made and tested. The result of vehicle tests on road demonstrates that the method is efficient and accurate to monitor tire pressure. The research has positive potential for developing products.

Applying Neural Networks for Tire Pressure Monitoring Systems

A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire.A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data.Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks(RNN-LSTM)and a convolutional neural network(CNN)developed in Python with Tensorflow.Bayesian Optimization via SigOpt was used to optimize training and model parameters.The predictive accuracy and training speed of the two models with various parameters are compared.Finally,future work and improvements are discussed.

Tire Pressure Monitoring System Using SoC and Low Power Design

This paper presents the tire pressure monitoring system (TPMS) by using the system on chip (SoC) mixed signals with the help of Bluetooth transmission and in advantage of low power consumption design. This is to monitor the variations in temperature and pressure of the vehicle’s tire, and the TPMS system is involved. It improves the driver’s safety by automatically detecting the tire pressure and temperature and then warning signal is sent to driver to take a measure, which prevents from accident. The proposed system of tire pressure monitoring system using SoC increases the speed of indication time to the driver by using mixed signals. The inflation of the tire can be avoided by preventing from high temperature and high pressure. Limitation of temperature and pressure in the previous system is also elongated i.e. temperature from 40℃ to 125℃ and pressure from 0 to 750 Kpa. Sensors, wireless communication (Bluetooth dongle) and SoC unit are used to design the low power TPMS. Quantitative results are taken and the analogy between temperature and pressure is also verified. The tested results proved by need of the practical system. Signal conditioning voltage and SoC unit is the trace for low power design TPMS. Finally, the performance of the system is tested and executed by using proteus software given as a real time application.

Modeling and analytical calculation of road damage coefficient considering tire pressure and damping of vehicles

The road damage coefficientβis a significant indicator to estimate the degree of the road damage caused by vehicles.The existing calculation method ofβis not convenient for the engineering application.To effectively evaluate the damage degree and facilitate the engineering application,this paper studied the simple and reliable analytical calculation method ofβ.Firstly,a dynamic model of the vehicle–road system was created.The tire pressure and the tire damping were considered in the model.Moreover,the relationship between the tire vertical stiffness and the tire pressure is approximated as a linear function.Secondly,based on the dynamic model,according to the definition ofβ,a concise analytical formula ofβwas derived and verified by numerical simulations.The relative errors of the analytical calculation results are all less than 0.1%.Thirdly,the influences of the tire pressure p,the damping ratioξs of the suspension system,and the damping ratioξt of the wheel system onβwere analyzed.Moreover,based on the analytical formula ofβ,a mathematical model of the optimal damping matching for the suspension system was established and a case study was also given.The research results show that the larger the tire pressure p,the larger the value ofβis.For each p,there is an optimal damping ratioξs.If the tire damping is ignored,it will lead to the design error forξs.Finally,some important conclusions were drawn.The analytical formula ofβand the conclusions can provide valuable references for the analysis of the road damage and the initial design of vehicle suspensions.

Simulation of vehicle braking behavior on wet asphalt pavement based on tire hydroplaning and frictional energy dissipation

To investigate the influence of wet conditions on vehicle braking behavior,a numerical-analytical method was proposed for the simulation of tire hydroplaning and frictional energy dissipation. First, a finite element model of tire hydroplaning was established using the coupled EulerianLagrangian method,including a pneumatic tire model and a textured asphalt pavement model. Then,the frictional force on the tire-pavement interface at different speeds was calculated by the model. Based on vehicle braking mechanism and frictional energy dissipation,a calculation method for braking distance was proposed based on a three-stage braking process. The proposed method was verified by comparing the calculated hydroplaning speed and braking distance with field test results.Then,vehicle braking distances and wet friction coefficients were calculated under different conditions. The results show that thinner water film,a more complex tread pattern and higher tire inflation pressure all contribute to the vehicle braking performance; moreover, the pavement texture has obvious influence on vehicle braking behavior,especially at a high speed. The proposed method shows great effectiveness in predicting vehicle braking behavior on wet asphalt pavements.