Tire Adhesion Estimation Using Thermomechanical Models at Low Load
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Solution Overview
Problem
Existing methods for estimating tire-to-ground adhesion are inadequate, particularly under low-load conditions, and lack the ability to provide preventive estimates, leading to ineffective corrective actions in vehicle safety systems.
Innovation Solution
A method that estimates tire adhesion potential using a vehicle model and thermomechanical tire model, combined with statistical comparison and Bayesian or Monte-Carlo Markov Chain methods, to account for thermal and other parameters, enabling real-time determination of adhesion coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sensorless methods are used to estimate adhesion, then cost and industrial intrusiveness are reduced, but measurement precision deteriorates under low-load conditions
Solution Approach 1:
The method segments the adhesion estimation problem into two distinct phases: a learning phase where the system collects and stores adhesion data under various conditions, and an operational phase where pre-established models are used for real-time estimation. This segmentation allows the system to achieve high precision without requiring complex sensors during normal operation, thus resolving the contradiction between measurement precision and ease of manufacture.
Solution Approach 2:
The system performs preliminary actions by collecting adhesion data and establishing statistical models during a learning phase before actual vehicle operation. This pre-processing of information creates lookup tables and models that enable accurate real-time estimation without requiring complex sensing equipment during critical moments, thereby achieving high measurement precision with simple implementation.
2Device complexity
If existing sensorless methods are used, then equipment complexity is reduced, but reliability deteriorates when load is below 40% of maximum
Solution Approach 1:
The method changes the approach from direct real-time measurement to using pre-established statistical models that account for various parameters including temperature, vehicle speed, and load conditions. By storing adhesion coefficients under different conditions during a learning phase and retrieving appropriate values during operation, the system achieves reliable estimation across all load conditions including low-load scenarios below 40% of maximum, while maintaining simple equipment.
Solution Approach 2:
The invention introduces statistical models and lookup tables as intermediaries between the physical tire-ground interaction and the adhesion estimation. These intermediaries, established during a learning phase, enable reliable estimation under all conditions by mapping observed parameters to adhesion coefficients, thereby achieving high reliability without increasing equipment complexity.
3Productivity
If methods focus on estimating adhesion when attained or close to being attained, then immediate corrective action can be triggered, but ability to provide preventive estimate deteriorates
Solution Approach 1:
The system performs preliminary estimation of adhesion potential using the learned statistical models before the vehicle actually reaches critical adhesion conditions. By continuously estimating adhesion based on current temperature, speed, and load parameters against the pre-established models, the system can provide preventive warnings and allow behavioral modifications before skidding occurs, while still maintaining the capability for immediate corrective action when adhesion is actually attained.
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
This approach provides a reliable and accurate estimation of tire adhesion potential, even at low loads, allowing for preventive measures to avoid skidding and improving vehicle safety by considering thermal effects and other parameters.
Implementation Method 1
Another method uses a tire model but does not take into account the thermal characteristics of the tire, thus falsifying the values determined for low loads
Data Source
AI summary
The rolling parameters of a tire on a rolling surface are evaluated, and more specifically a tire's adhesion potential on a rolling surface is estimated. A method and a system enabling such estimation are disclosed.