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）. ...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.展开更多
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 g...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.展开更多
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 monit...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.展开更多
基金supported by the Opening Foundation of State Key Laboratory of Automobile Safety and Energy,Tsinghua University,China(Grant No. KF2005-11,Grant No.KF2007-09)
文摘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.
文摘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.
文摘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.
