TPM Sensor Auto-Location via ABS Confidence Interval Analysis
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Solution Overview
Problem
Existing tire pressure monitoring systems face challenges in accurately auto-locating tire pressure monitoring sensors due to irregularities in ABS data caused by factors like road noise, vehicle vibrations, and communication delays, which can lead to biased wheel location determination.
Innovation Solution
The system employs confidence interval analysis based on histogram patterns of ABS sensor data to correlate radio frequency transmissions from TPM sensors, identifying the wheel location by distinguishing between normal and random distribution patterns, thereby reducing the impact of extreme data points and ensuring precise sensor positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis of ABS data is used to determine wheel location, then auto-location can be performed, but extreme data points caused by nuisance factors bias the location determination
Solution Approach 1:
The patent extracts only the relevant portion of ABS data by using a rolling window to capture data within a specific time period around the RF transmission event. This extraction approach isolates the meaningful data from the broader data stream, preventing extreme values from unrelated time periods from biasing the statistical analysis.
Solution Approach 2:
The system performs preliminary actions by continuously maintaining a rolling window of ABS data in advance of the auto-location determination. This pre-captured data buffer allows the system to quickly analyze relevant ABS data when an RF transmission is received, without needing to collect data in real-time during the location determination process.
2Measurement precision
If ABS tooth count data is analyzed to correlate wheel positions, then TPM sensor locations can be identified, but counter clockwise wheel movement causes the mean of correlated ABS tooth counts to shift
Solution Approach 1:
The patent applies dynamics by making the analysis window adaptive rather than fixed. The rolling window dynamically adjusts its content based on the timing of RF transmissions and wheel rotation events, allowing the statistical analysis to remain valid regardless of whether the wheel rotates clockwise or counter clockwise. This dynamic approach enables the system to handle bidirectional wheel movement while maintaining accurate TPM sensor location identification.
3Measurement precision
If a rolling window of ABS data is maintained for correlation analysis, then precise timing can be achieved, but data storage and processing requirements increase
Solution Approach 1:
The patent applies partial action by maintaining a rolling window that covers only the necessary time period for accurate correlation analysis, rather than storing all historical ABS data. The window size is optimized to be sufficiently large to capture the relevant wheel rotation events for precise timing, but not excessively large to create unnecessary storage burden. This selective data retention achieves the required timing precision with minimal data storage.
Data Source
AI summary
Auto-location systems and methods of tire pressure monitoring sensor units arranged with a wheel of a vehicle detect a predetermined time (T1) when a wheel phase angle reaches angle of interest using a rim mounted or a tire mounted sensor. The systems and methods transmit a radio frequency message associated with a wheel phase angle indication. The wheel phase angle indication triggers wheel phase and/or speed data such as ABS data at the predetermined time (T1) to be stored. A correlation algorithm is executed to identify the specific location of a wheel based on the wheel phase and/or speed data at the predetermined time (T1). TPM sensor parameters from a tire pressure monitoring sensor unit are assigned to the specific location of the wheel based on a confidence interval width analysis of the ABS data at the predetermined (T1). The confidence interval width analysis identifies the specific location of the wheel whose ABS sensor shows a lowest confidence interval width as a result of a normal distribution pattern or similar pattern. The confidence interval width analysis may calculate a weighted cumulative confidence interval width for the ABS data which experience rollback events.