Tire Wear Estimation Model Using Turn Radius and Travel Data
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Solution Overview
Problem
Existing tire wear estimation systems, such as JP2018-158722 A, do not accurately account for factors like turns and timing of data acquisition, leading to suboptimal tire wear estimation, particularly for large vehicles like trucks.
Innovation Solution
An arithmetic model generation system that acquires tire data including temperature, pressure, and position information, using a learning-type model like a neural network to calculate tire wear amount, and updates the model based on measured wear data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If wheel position and drivetrains are used as vehicle effects in tire wear estimation, then the estimation system can be implemented with existing data, but the estimation accuracy deteriorates for large vehicles like trucks where these factors have less impact
Solution Approach 1:
The patent changes the parameters used in the arithmetic model from generic wheel position and drivetrain data to vehicle-specific parameters such as turn radius, travel distance, speed, and vertically applied load. This allows the model to adapt to different vehicle types (trucks vs. passenger cars) and accurately reflect the actual factors influencing tire wear for each vehicle category.
2Adaptability or versatility
If generic vehicle effects are used in the arithmetic model, then the model can be applied broadly across different vehicles, but the precision of wear amount calculation deteriorates due to lack of vehicle-specific factors
Solution Approach 1:
The patent implements a dynamic arithmetic model that adapts to different vehicle types and operating conditions. The model dynamically selects and weights parameters such as turn radius, travel distance, speed, and vertically applied load based on the specific vehicle and its operational context, rather than using static generic factors. This enables both broad applicability and high precision for different vehicle categories.
3Measurement precision
If turn radius and travel distance are incorporated into the arithmetic model, then the wear estimation accuracy improves, but the device complexity increases due to additional sensors and data processing requirements
Solution Approach 1:
The patent leverages multi-functional existing vehicle systems to obtain required parameters. The position information acquisition unit can derive turn radius and travel distance from GPS or inertial sensors already present in modern vehicles. The tire information acquisition unit obtains pressure and temperature data from existing tire pressure monitoring systems (TPMS). This approach incorporates sophisticated wear parameters without proportionally increasing system complexity.
4Measurement precision
If multiple parameters including turn radius, speed, and vertically applied load are used in the arithmetic model, then the wear amount calculation becomes more accurate, but the amount of information to be processed increases
Solution Approach 1:
The system automatically collects, processes, and integrates multiple parameters (turn radius, travel distance, speed, vertically applied load, tire pressure, temperature) through an autonomous arithmetic model. The model self-adjusts weights and relationships between parameters based on their actual influence on tire wear, reducing the need for manual data curation and minimizing information loss while managing processing loads efficiently.
Data Source
AI summary
An arithmetic model generation system includes a tire information acquisition unit, a position information acquisition unit, a wear amount calculator, and an arithmetic model update unit. The tire information acquisition unit acquires tire data including a temperature and pressure of a tire. The position information acquisition unit acquires position data for a vehicle on which the tire is mounted. The wear amount calculator includes an arithmetic model that generates an estimated wear amount of at least one groove of the tire, the wear amount calculator calculating the estimated wear amount of at least one groove of the tire by using the arithmetic model by inputting the tire data and the position data. The arithmetic model update unit updates the arithmetic model based on a wear amount measured by a tire measurement device external to the vehicle and the estimated wear amount.