Tire Rolling Resistance Estimation Under Transient Temperature Changes
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Solution Overview
Problem
Existing tire rolling resistance estimation systems struggle to accurately predict rolling resistance under dynamic driving conditions due to the lack of a thermal model that accounts for tire temperature changes during transient operations.
Innovation Solution
A tire rolling resistance estimation system that includes a tire sensor unit to measure inflation pressure and temperature, a processor for data processing, and modules for determining steady state and real-time rolling resistance coefficients using multiple linear regression and semi-empirical models, accounting for tire wear, load, and wheel speed, along with a thermal model to estimate real-time rolling resistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rolling resistance is estimated using lab testing at fixed ambient temperature, then measurement precision is improved, but adaptability to transient driving conditions deteriorates
Solution Approach 1:
The system changes the parameter of ambient temperature from a fixed lab value to a dynamically varying value that reflects actual driving conditions. The thermal model continuously updates the ambient temperature parameter based on driving history and environmental factors, allowing the rolling resistance estimation to adapt to transient conditions while maintaining measurement precision through controlled parameter variation.
Solution Approach 2:
The system transitions from a static lab testing approach to a dynamic estimation approach that continuously adapts to changing driving conditions. The thermal model dynamically adjusts the ambient temperature parameter in real-time, and the rolling resistance coefficient is continuously updated based on current tire temperature and other varying parameters, enabling the system to maintain accuracy under transient conditions.
2Adaptability or versatility
If a thermal model accounting for tire temperature prediction is added, then adaptability to transient conditions is improved, but device complexity increases
Solution Approach 1:
The thermal model acts as an intermediary component that bridges the gap between simple sensor measurements and complex rolling resistance estimation. It processes temperature data and environmental factors to generate a corrected ambient temperature parameter, which is then used by the rolling resistance coefficient calculation module, thereby enabling transient condition adaptability without requiring direct complex modeling in the main estimation algorithm.
Solution Approach 2:
The system segments the rolling resistance estimation process into distinct functional modules: a thermal model module that handles temperature prediction, a steady-state coefficient determination module, and a real-time coefficient calculation module. This segmentation allows each module to focus on specific aspects of the problem, reducing overall system complexity while improving adaptability to transient conditions.
3Adaptability or versatility
If real-time estimation under transient conditions is achieved, then adaptability is improved, but measurement precision may deteriorate
Solution Approach 1:
The system implements feedback through the thermal model that continuously monitors tire temperature and adjusts the ambient temperature parameter accordingly. This feedback mechanism ensures that the rolling resistance estimation remains precise under transient conditions by compensating for temperature effects in real-time, thereby maintaining measurement precision while achieving adaptability to changing driving conditions.
Data Source
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AI summary
A tire rolling resistance estimation system and method is disclosed. The system (10) estimates a real-time rolling resistance coefficient (50) of a tire (12) supporting a vehicle (14). The system (10) includes: a tire sensor unit (26) mounted to the tire (12), the tire sensor unit (26) measuring an inflation pressure of the tire and a temperature of the tire; a processor (40) in electronic communication with the tire sensor unit (26); a steady state coefficient determination module (54) in electronic communication with the processor (40) for receiving a plurality of inputs and for determining a steady state rolling resistance coefficient of the tire; a steady state tire temperature module (90) in electronic communication with the processor (40) for receiving the steady state rolling resistance coefficient of the tire from the steady state coefficient determination module (54) and for estimating a steady state tire temperature of the tire; and a real-time rolling resistance coefficient module (92) in electronic communication with the processor (40), the real-time rolling resistance coefficient (92) module receiving the steady state tire temperature of the tire from the steady state tire temperature module and estimating a real-time rolling resistance coefficient of the tire from the steady state rolling resistance coefficient and a difference between the steady state tire temperature and a current tire temperature from the tire sensor unit (26).