Tire Pressure Monitoring Data Filtering via GNSS and Inertial Sensors
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Solution Overview
Problem
Current tire pressure monitoring systems face reduced accuracy due to the lack of a true reference speed when determining vehicle longitudinal speed, which affects the quality of tire radius and pressure measurements.
Innovation Solution
A method that filters data by combining tire radius-dependent variables from wheel speed sensors and inertial sensor data, using GNSS signals, and employs signal processing techniques like Kalman filters to correct errors and increase information content, thereby improving the accuracy of tire pressure monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle longitudinal speed is determined from wheel speed sensors or inertial sensor data, then tire radius can be determined, but measurement accuracy deteriorates due to lack of true reference speed
Solution Approach 1:
The patent combines multiple measurement principles (wheel speed sensors, inertial sensor data, and GNSS signals) to determine vehicle longitudinal speed. By merging these different data sources through filtering processes, the system compensates for the limitations of individual sensors and achieves more accurate tire radius measurements without requiring a true reference speed.
Solution Approach 2:
The patent introduces GNSS signals as an intermediary measurement source to provide an independent reference for vehicle longitudinal speed. This intermediary data source mediates between the wheel speed sensors and inertial sensors, enabling cross-validation and error correction to improve measurement accuracy.
2Measurement precision
If multiple measurement principles are combined to improve tire radius accuracy, then measurement quality increases, but system complexity increases
Solution Approach 1:
The filtering system performs multiple functions simultaneously: it processes data from wheel speed sensors, inertial sensors, and GNSS receivers; determines vehicle longitudinal speed; calculates tire radius; and corrects measurement errors. This multi-functional approach consolidates what could be separate complex systems into a unified processing framework.
Solution Approach 2:
The patent changes the processing parameters by applying different filtering techniques (such as Kalman filters) to combine measurements from multiple sources. By adjusting filtering parameters and weighting different measurement principles dynamically, the system achieves high measurement accuracy without requiring equally complex hardware for each sensor type.
Data Source
AI summary
A method for filtering data in a tire pressure monitoring system for a vehicle includes the steps of: recording a variable that is dependent on the tire radius for a wheel of the vehicle, and filtering the variable that is dependent on the tire radius on the basis of driving dynamics data and/or a global navigation satellite system signal, called GNSS signal below. A vehicle control system and a vehicle having the control system are also described.