Self-Learning Tire Position Detection for Multi-Axle Vehicles
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Solution Overview
Problem
Current wheel location systems for multi-axle vehicles do not efficiently and automatically determine tire positions, leading to challenges in maintenance planning and minimizing downtime, especially in commercial truck fleets where tire pressure loss detection and replacement are critical.
Innovation Solution
A self-learning methodology using piezoelectric sensors integrated with tires to generate signals for In Tire Electronic Modules (ITEMs), which transmit data to an Electronic Control Unit (ECU) for analysis, allowing for automatic determination of tire positions without external mechanical input, through phases of steer-drive-trailer-left-right identification and inside-outside and fore-aft discrimination based on revolution counting, speed, and load changes during normal vehicle maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tire monitoring systems are used with unique tire ID association, then tire pressure and position can be monitored, but the system complexity increases and automatic position determination is not achieved
Solution Approach 1:
The system enables self-service by allowing the tire monitoring system to automatically determine its own wheel positions through self-learning during normal vehicle operation. The ECU analyzes data from multiple sensors and autonomously identifies tire positions without requiring external mechanical input or manual configuration, making the system self-configuring and reducing overall system complexity.
Solution Approach 2:
The patent replaces mechanical identification methods with electronic sensor-based detection. Instead of using mechanical tags or manual positioning systems, the invention uses electronic sensors (piezoelectric sensors, revolution counters) and electronic control units that analyze electrical signals and data patterns to automatically determine tire positions, thereby reducing mechanical complexity.
2Productivity
If manual tire position identification is used, then system complexity is reduced, but maintenance planning efficiency and downtime minimization are compromised
Solution Approach 1:
The system implements continuous feedback by constantly monitoring tire pressure, temperature, and revolution data, then feeding this information back to the ECU which automatically correlates the data with specific wheel positions. This closed-loop feedback mechanism enables real-time identification of which tires need maintenance, improving maintenance planning efficiency and reducing vehicle downtime.
Solution Approach 2:
The system performs preliminary action by continuously monitoring and identifying tire positions and conditions before maintenance is actually needed. The automatic position determination and pressure loss detection occur during normal operation, allowing maintenance to be planned in advance rather than reacting to failures, thus minimizing downtime.
3Reliability
If tire sensors transmit data continuously, then real-time monitoring is achieved, but energy consumption increases
Solution Approach 1:
The system uses periodic action by having sensors transmit data at specific intervals or under specific conditions rather than continuously. The piezoelectric sensors and revolution counters operate periodically during vehicle motion, and the ECU processes data in periodic cycles, reducing overall energy consumption while maintaining real-time monitoring capability during operation.
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
Enables efficient and automatic tire position determination on multi-axle vehicles, reducing downtime by allowing for precise maintenance planning and timely replacement, minimizing operational disruptions in commercial fleets.
Implementation Method 1
A self-learning methodology using piezoelectric sensors integrated with tires to generate signals for In Tire Electronic Modules (ITEMs)
Data Source
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Figure 3
AI summary
Disclosed is an apparatus and methodology for identifying tire locations associated with a vehicle (10). Sensed variations in tire related parameters are measured as a vehicle traverses a known or ascertainable travel path (fig. 3). Data accumulated over one or more measurement windows may be analyzed to determine the location of each individual tire (20) associated with a vehicle (10). Measurements and accumulation of data may be initiated upon detection of a stationary vehicle state exceeding a predetermined time, a predetermined lateral acceleration, and/or a predetermined vehicle speed.