Tire Pressure Detection via Dynamic Wheel Speed Resampling
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Solution Overview
Problem
Conventional Tire Pressure Monitoring Systems (TPMS) using the Resonance Frequency Method face challenges in accurately detecting tire pressure changes under varying running conditions due to noise interference and limited computational resources, especially at lower speeds, leading to inaccurate resonance frequency calculations and poor detection of tire pressure abnormalities.
Innovation Solution
An apparatus and method that resample wheel speed signals using a predetermined sampling cycle and calculate the number of teeth passed per cycle to obtain a static wheel speed signal, employing a simple calculation method that reduces noise influence and maintains accuracy across different speeds, utilizing formulas to accurately estimate the resonance frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the Resonance Frequency Method is used to detect tire pressure, then detection capability for simultaneous deflation is improved, but measurement precision deteriorates under varying running conditions due to noise interference
Solution Approach 1:
The patent applies dynamics by making the sampling cycle adaptive to vehicle speed. The sampling cycle is dynamically adjusted based on detected vehicle speed, ensuring that resonance frequency calculations remain accurate across varying running conditions. This resolves the contradiction by allowing the system to adapt its measurement parameters to maintain precision while preserving the ability to detect tire pressure abnormalities.
Solution Approach 2:
The patent changes the sampling cycle parameter based on vehicle speed to maintain measurement precision. By adjusting this key parameter dynamically, the system preserves accurate resonance frequency calculation under varying conditions while maintaining the detection capabilities of the RFM method.
2Measurement precision
If complex frequency analysis methods are used to improve detection accuracy, then measurement precision is improved, but device complexity increases due to computational resource requirements
Solution Approach 1:
The patent applies partial action by using a simplified frequency analysis approach that focuses only on detecting resonance peaks in the wheel speed signal, rather than performing comprehensive spectral analysis. This partial analysis is sufficient for tire pressure detection and significantly reduces computational requirements while maintaining adequate measurement precision.
Solution Approach 2:
The patent uses computationally inexpensive processing methods that can be executed quickly on limited in-vehicle resources. By employing simple peak detection algorithms rather than complex Fourier transforms or other heavy computational methods, the system achieves acceptable detection accuracy with minimal computational burden.
3Device complexity
If the sampling cycle is extended to reduce computational load, then device complexity is reduced, but measurement precision deteriorates at lower vehicle speeds
Solution Approach 1:
The patent makes the sampling cycle dynamic rather than fixed. The sampling cycle is adjusted based on detected vehicle speed, being shorter at lower speeds to maintain precision and longer at higher speeds to reduce computational load. This dynamic adaptation resolves the contradiction between computational efficiency and measurement accuracy across different operating conditions.
Solution Approach 2:
The patent changes the sampling cycle parameter according to vehicle speed conditions. By adjusting this parameter dynamically, the system optimizes the balance between computational load and measurement precision for each specific operating condition, preventing precision deterioration at low speeds while maintaining reasonable computational requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed solution enables secure and accurate calculation of resonance frequencies under various conditions, improving tire pressure detection accuracy and reducing computational load, with consistent performance across different vehicle speeds and road surfaces.
Implementation Method 1
a tire having a decreased pressure has a different wheel speed signal frequency characteristic to thereby detect a difference from a normal pressure... a peak of the coupled resonance vibration (resonance peak) appears at the lower frequency-side in the case of a decreased pressure
Data Source
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AI summary
A device for detecting a tire having a decreased pressure comprises a rotation speed information detection apparatus, including: a wheel sensor for detecting passage of teeth of a gear provided in association with tires of a vehicle; a number-of-teeth calculation means for calculating the number of teeth of the gear passed during a sampling cycle having a fixed duration set in advance; and a wheel speed calculation means for regularly calculating rotation speed information of the tire with using the number of teeth calculated. The number-of-teeth calculation means is configured to use a ratio between time information at the point and a remaining time until the time at which the next sampling cycle is started is reached to thereby calculate a fractional number related to the remaining incomplete and not fully counted teeth, to calculate the number of teeth passed during the sampling cycle as a fractional number. The wheel speed calculation means is configured to calculate tire rotation speed information based on an interval between neighboring teeth in the gear, the number of full teeth passed during the sampling cycle, the fractional number of remaining teeth calculated by the number-of-teeth-calculation means, and the sampling cycle. A wheel acceleration signal is determined based on the sequence of speed values, and a frequency characteristic is determined by a spectral analysis of this wheel acceleration signal. This improves the computation accuracy and allows to determine a tire having a decreased pressure from this frequency characteristic.