Tire Grip Estimation Using Friction Probability Distribution
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Solution Overview
Problem
Existing tire monitoring systems rely on reactive real-time estimations of tire grip, which require inducing slip through acceleration or braking, limiting their accuracy and usefulness for proactive vehicle control systems.
Innovation Solution
A method that generates a proactive real-time estimation of tire grip by combining data from tire-mounted sensors, vehicle systems, and internet data using a grip estimation module to calculate a friction probability distribution, eliminating the need for slip induction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reactive real-time estimation of tire grip is used, then tire grip can be estimated during vehicle operation, but slip induction through acceleration or braking is required which limits accuracy and usefulness
Solution Approach 1:
The system performs preliminary actions by inducing slip through acceleration or braking maneuvers before the actual grip estimation is needed. This proactive approach allows the system to gather data about tire-road interaction under controlled slip conditions, enabling more accurate grip estimation without requiring slip induction during normal operation.
Solution Approach 2:
The system uses an intermediary approach by introducing controlled slip conditions as a mediator between the tire and road surface. This controlled slip acts as an intermediary state that provides measurable data about friction characteristics, which then informs the grip estimation algorithm without requiring continuous slip induction.
2Reliability
If slip induction through acceleration or braking is used for grip estimation, then real-time tire grip can be measured, but the method is reactive rather than proactive and requires exciting the tire
Solution Approach 1:
The system performs preliminary actions by inducing slip through acceleration or braking maneuvers before the actual grip estimation is needed. This proactive approach allows the system to gather data about tire-road interaction under controlled slip conditions, enabling more accurate grip estimation without requiring slip induction during normal operation.
Solution Approach 2:
The system implements feedback by using the results of controlled slip induction to continuously refine and update the grip estimation model. The measured slip and corresponding vehicle dynamics data are fed back into the estimation algorithm, improving the reliability of future grip predictions without requiring repeated slip induction events.
3Measurement precision
If laboratory or track testing is used to measure peak grip level, then comprehensive tire performance data can be obtained, but real-time estimation during vehicle operation is not achieved
Solution Approach 1:
The system creates a simplified copy or model of the laboratory testing environment by implementing controlled slip induction maneuvers during vehicle operation. This copy allows the system to replicate the essential measurements of peak grip level without requiring actual laboratory or track testing, providing real-time estimates based on modeled test conditions.
Solution Approach 2:
The system replaces the mechanical testing infrastructure of laboratories and tracks with an onboard vehicle system that performs equivalent measurements using the vehicle's own propulsion and braking capabilities. This substitution eliminates the need for external testing facilities while maintaining measurement accuracy through controlled slip induction.
Data Source
AI summary
A method for estimating a grip of a tire supporting a vehicle includes generating a first set of data from a tire-mounted sensor unit, generating a second set of data from the tire-mounted sensor unit and from data obtained from the vehicle, and generating a third set of data from data obtained from the vehicle and from the Internet. A grip estimation module is provided. The first, second and third sets of data are received in the grip estimation module. A friction probability distribution is calculated with the grip estimation module using the first, second and third sets of data, and the friction probability distribution is input into at least one vehicle system.