Tire Traction Prediction Using Real-Time Wear Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current tire wear prediction methods are inefficient, relying on computationally expensive finite element analysis that takes weeks to months to simulate wear rates, and fail to provide real-time feedback on tire performance and traction capabilities, leading to potential safety issues due to inadequate tread depth.
Innovation Solution
A computer-implemented method for real-time tire performance modeling and feedback, utilizing Bayesian estimation and a brush-type tire wear model to predict tire wear status based on collected vehicle and tire data, providing alerts and recommendations for optimal tire maintenance and operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element analysis is used to predict tire wear, then prediction accuracy is improved, but computation time increases to weeks or months
Solution Approach 1:
The patent transforms the complex finite element analysis into a simplified parameter-based model that uses key tire and vehicle parameters to predict wear rates. This parameter change approach maintains prediction accuracy while reducing computation time from weeks to real-time, as the model uses established relationships between parameters rather than performing full-scale simulations
Solution Approach 2:
The patent extracts only the essential elements needed for wear prediction from the complete finite element analysis framework. By taking out and focusing on the critical parameters and relationships that drive tire wear, the model achieves accurate predictions without the computational overhead of full FEA simulations
2Reliability
If real-time tire performance monitoring is implemented, then safety is improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the simplified wear prediction model continuously monitors tire conditions and provides real-time performance information. This feedback loop enables safety monitoring without complex hardware by using software-based parameter tracking and prediction algorithms that process available vehicle data
Solution Approach 2:
The patent creates a virtual model (digital twin) of the tire wear process that replicates the physical tire's degradation behavior. This virtual copy allows real-time monitoring and prediction without adding physical sensors or complex hardware to the actual tire, reducing system complexity while maintaining monitoring capabilities
3Measurement precision
If comprehensive tire data collection is performed, then prediction accuracy is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts and utilizes only the most critical data elements from comprehensive tire measurements. By identifying and processing only the key parameters that significantly influence wear predictions, the model achieves high accuracy while minimizing data processing requirements and energy consumption
Solution Approach 2:
The patent applies different levels of data processing to different parameters based on their importance. Critical parameters receive detailed processing while less important parameters use simplified processing methods, optimizing the balance between prediction accuracy and computational energy requirements
Data Source
AI summary
A system and method are provided for estimating and applying vehicle tire traction. Vehicle data (e.g., movement and location-based data) and tire sensor data are collected at a vehicle and transmitted to a remote computing system (e.g., cloud server). A wear status is determined, and traction characteristics determined for at least one tire, based at least on the vehicle data and the determined tire wear status. The predicted tire traction characteristics are transmitted from the remote computing system to an active safety unit associated with the vehicle, or a fleet management system, wherein the recipient is configured to modify vehicle operation settings based on at least the predicted tire traction characteristics. A maximum speed for the vehicle may be defined by the recipient, or a minimum following distance where, e.g., the vehicle is one of multiple vehicles in a defined platoon.