Simulative research on the tire torsional vibration and its vehicle relevant influencing factors

Autoren

Dipl.-Ing. Wenrui Han, Yi Guo, Prof. Dr.-Ing. Günther Prokop, Lehrstuhl für Kraftfahrzeugtechnik, Technische Universität Dresden;
Dr. Thomas Roscher, AUDI AG

Jahr

2019

Zusammenfassung

Tire pressure as an important vehicle parameter has a great impact on the vehicle safety, ride comfort, driving dynamics and fuel economy. Every registered vehicle in the USA needs to be equipped with a tire pressure monitoring system from 2007. This legal requirement has been published in the EU since 2012.
The tire pressure monitoring system (TPMS) can be divided into direct and indirect systems. The indirect tire pressure monitoring system (iTPMS) is a cost friendly solution for the tire pressure monitoring. In this system the wheel speed signal has been used for detection of tire pressure loss.
In iTPMS 2 algorithms have been used. One algorithm is the comparison of the rolling radius of 4 wheels, the other one is the analysis of the natural frequency of wheel speed signal. For 1-3 wheels pressure loss scenario (puncture) the both algorithms can be used. For the 4 wheels pressure loss scenario (diffusion) just the second algorithm can be used, because in this case there is no significant difference of the rolling radius between four wheels. At pressure loss the natural frequency of the wheel speed signal in a frequency band of 30-60 Hz will decrease, This pressure-dependent natural frequency can be used to detect the four wheels pressure loss.
Tire pressure is one of the most important influencing factors for the pressure-dependent eigenmode. However, this eigenmode can also be influenced by other factors such as suspension components.
The iTPMS is based on the wheel speed signal, which can represent the tire torsional vibration. In order to get a better understanding of the influence of suspension on the tire torsional vibration and realize an early predication of the iTPMS performance in the concept phase. The influencing factors can be investigated via simulation. The models, which have been used in this investigation, will be shown in the next sections.

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