Tire Thermal Modeling for Accurate Vertical Load Prediction
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Solution Overview
Problem
Current tire wear prediction systems lack accurate and reliable vertical load measurement, which is crucial for predicting tire wear and ensuring safe driving conditions.
Innovation Solution
A system and method that predict vertical load on a vehicle tire using typical sensor measurements such as vehicle speed, ambient temperature, tire inflation pressure, and tire contained air temperature, by generating a model based on thermal characteristics and tire-specific time constants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vertical load sensors are used, then measurement accuracy is improved, but system cost and reliability deteriorate due to prohibitively expensive and unreliable sensors
Solution Approach 1:
The patent uses tire temperature as an intermediary parameter to indirectly measure vertical load. Instead of directly measuring vertical load with expensive and unreliable sensors, the system measures tire temperature (which is easier to measure reliably) and uses it as a mediator to infer vertical load through pre-established thermal characteristics and modeling, thereby resolving the contradiction between measurement accuracy and sensor reliability
Solution Approach 2:
The patent replaces the mechanical/physical vertical load sensing system with a thermal-based measurement and computation system. By substituting direct mechanical load measurement with thermal field measurement and mathematical modeling, the system achieves reliable vertical load prediction without requiring expensive and unreliable direct load sensors
2Device complexity
If typical sensor measurements are used, then system complexity is reduced, but vertical load prediction accuracy deteriorates due to missing critical information
Solution Approach 1:
The patent transforms the approach by changing from direct measurement parameters (vertical load) to indirect thermal parameters (tire temperature) combined with operational parameters (speed, pressure). By changing the measurement parameter from mechanical load to thermal state, the system achieves accurate vertical load prediction using simpler, more readily available sensor data
Solution Approach 2:
The patent performs preliminary characterization of tire thermal characteristics under various operating conditions before actual use. By pre-establishing the relationship between temperature, speed, pressure, and vertical load through testing and modeling, the system enables accurate real-time vertical load prediction during operation without requiring complex real-time computation or additional sensors
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 and reliable prediction of vertical load, improving tire wear prediction and ensuring safer driving conditions by addressing the limitations of existing systems.
Implementation Method 1
retrievably storing one or more thermal characteristics for a particular vehicle-tire combination, the thermal characteristics determined as corresponding to a range of temperature values further correlated with a plurality of operating conditions
Implementation Method 2
generating (e.g., empirically training) a model for predicting transient temperature behavior based on one or more tire-specific time constants
Data Source
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AI summary
A computer-implemented method as disclosed enables predicting of vertical loads on a vehicle tire. Thermal characteristics for a particular vehicle-tire combination are retrievably stored, the thermal characteristics determined as corresponding to a range of temperature values further correlated with a plurality of operating conditions, and the plurality of operating conditions comprising at least a vertical load. A model for predicting transient temperature behavior is generated (e.g., empirically trained) based on one or more tire-specific time constants. During operation of the vehicle-tire combination and responsive to at least a first temperature value, a computing device residing on the vehicle or otherwise cloud-based in nature is configured to determine a predicted vertical load based on a predicted second temperature value from the model and further based on the retrievably stored one or more thermal characteristics.