Wheel Dynamic Radius Compensation for Tire Tread Depth Estimation
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Solution Overview
Problem
Existing tire wear monitoring systems fail to account for changes in tire behavior during vehicle use, leading to inadequate grip and safety risks, particularly in autonomous vehicles, due to reliance on factory-set compensation parameters that do not adapt to individual tire usage.
Innovation Solution
A method for determining a compensated dynamic radius of a vehicle wheel that updates compensation factors in real-time based on sensor data, using multi-linear regression to adapt to tire-specific changes during operation, incorporating variables like speed, pressure, and load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory-set compensation parameters are used for dynamic radius calculation, then the system is simple to implement, but the measurement precision deteriorates because the parameters do not adapt to individual tire usage and changes during vehicle operation
Solution Approach 1:
The system performs self-calibration by automatically determining compensation parameters through learning phases during vehicle operation. The microprocessor executes algorithms that autonomously adjust compensation parameters based on measured dynamic radius values and operational conditions, eliminating the need for manual factory calibration and enabling continuous adaptation to individual tire characteristics and wear patterns.
Solution Approach 2:
The system implements a feedback mechanism where measured dynamic radius values and operational conditions (speed, temperature, load) are continuously fed back to update compensation parameters. The microprocessor compares measured values with expected values and adjusts compensation parameters accordingly, creating a closed-loop system that continuously improves measurement precision through real-world operational data.
2Adaptability or versatility
If compensation parameters are updated in real-time during vehicle operation, then the adaptability improves for individual tire characteristics, but the loss of time increases due to continuous learning and calibration phases
Solution Approach 1:
The system performs preliminary calibration actions during manufacturing by storing initial compensation parameters in the microprocessor memory before the vehicle reaches the customer. This preliminary setup allows the system to begin operations with basic calibration data, reducing the initial learning time required and enabling faster deployment while still allowing for subsequent real-time adaptations.
Solution Approach 2:
The system implements periodic learning phases interspersed with normal operational phases. Instead of continuous calibration that would halt operations, the system periodically enters learning phases to update compensation parameters, then returns to normal operation. This periodic approach balances adaptation needs with operational continuity, minimizing time loss while maintaining adaptability.
3Reliability
If multiple sensor variables (speed, pressure, load, temperature) are continuously monitored and processed, then the reliability of tire wear monitoring improves, but the use of energy increases due to continuous sensing and computation
Solution Approach 1:
The system implements partial monitoring by selectively activating full sensor suites only when needed for calibration or anomaly detection. During normal operation, the system uses a reduced set of sensors and simplified processing algorithms, consuming less energy while maintaining adequate monitoring reliability. The system escalates to full monitoring mode only when reliability requirements demand it, such as during critical wear detection or calibration phases.
Data Source
AI summary
A method for determining a compensated dynamic radius of a wheel of a vehicle, the compensated dynamic radius being a function of: a raw dynamic radius, instantaneous values of variables, compensation factors specific to the variables. The compensation factors are obtained by a learning phase that includes: acquiring, when the vehicle is in operation and for each variable, values of the raw dynamic radius as a function of the evolution of the variables, calculating the compensation factors based on each of the values. Also disclosed is a method for estimating the depth of a tread of a tire and to a motor vehicle implementing the methods.