Steering Torque Brush Model for Low-Speed Tire-Road Friction Estimation
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Solution Overview
Problem
Current models fail to accurately estimate friction coefficients between a tire and a road surface for stationary or slow-rolling vehicles, which is crucial for predicting steering torques during low-speed maneuvers.
Innovation Solution
A method using sensors to measure wheel velocity and steering rate, combined with a brush model that describes steering torque across the tire-road contact patch, allowing for friction coefficient estimation by integrating vertical load distribution and relative motion, even when the vehicle is stationary or moving slowly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tire models are used for high-speed operation, then the dominant source of tire tread stress is deformation due to lateral force, but these models fail to accurately estimate friction coefficients for stationary or slow-rolling vehicles
Solution Approach 1:
The patent develops a dynamic brush model that adapts to different operating conditions by incorporating velocity-dependent terms. The model transitions from static friction analysis at zero velocity to dynamic friction analysis at higher velocities, allowing accurate friction coefficient estimation across the entire speed range from stationary to rolling conditions.
Solution Approach 2:
The patent changes the governing parameters of the tire model based on operating conditions. At low speeds, the model emphasizes steering rate and static friction parameters, while at higher speeds, it transitions to emphasize lateral force and deformation parameters, thereby achieving accurate friction estimation across all speed ranges.
2Measurement precision
If a brush model is used to describe steering torque across the contact patch, then friction sensitivity increases, but the complexity of integrating vertical load distribution and relative motion increases
Solution Approach 1:
The patent segments the tire contact patch into multiple discrete brush elements, each experiencing different vertical loads and relative motions. By integrating the contributions of individual brush elements across the contact patch, the model achieves high friction sensitivity while maintaining computational tractability through systematic decomposition of the complex integration problem.
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 accurate prediction of steering torques and friction sensitivity at low speeds, providing insights into surface conditions and improving steering system design and safety.
Implementation Method 1
friction estimation between the tire and roadway surface
Implementation Method 2
the elastic deformation of the rubber that is dependent upon the history of steering motion
Data Source
AI summary
A device for estimating a friction coefficient between a road surface and an automotive tire through determination of a steering torque during a steering maneuver of a slow-rolling or stationary vehicle includes a computer configured for constructing a brush model for a description of the steering torque across a contact patch between the tire and road surface. The steering torque is a torque acting on a steering axis required to overcome resistance to tire twisting on the road surface at a wheel velocity and a steering rate. The steering torque depends on a tire brush vertical load distribution and relative motion of tire brushes and the road surface. The device further includes sensors for measuring the wheel velocity and the steering rate and mechanism for measurements or estimation of the steering torque. The friction coefficient is estimated based on the measurements or estimation of the steering torque and the brush model.