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Physical Validation Testing of a Smart Tire Prototype for Estimation of Tire Forces
Technical Paper
2018-01-1117
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The safety of ground vehicles is a matter of critical importance. Vehicle safety is enhanced with the use of control systems that mitigate the effect of unachievable demands from the driver, especially demands for tire forces that cannot be developed. This paper presents the results of a smart tire prototyping and validation study, which is an investigation of a smart tire system that can be used as part of these mitigation efforts. The smart tire can monitor itself using in-tire sensors and provide information regarding its own tire forces and moments, which can be transmitted to a vehicle control system for improved safety. The smart tire is designed to estimate the three orthogonal tire forces and the tire aligning moment at least once per wheel revolution during all modes of vehicle operation, with high accuracy. The prototype includes two in-tire piezoelectric deformation sensors and a rotary encoder. Data from the sensor measurements are processed using a radial basis function neural network. The network is employed to estimate the tire forces and moment as developed at the wheel center. The data processing methodology is formulated first using virtual sensor measurements as computed by a tire finite element model. The smart tire has excellent performance when using virtual sensor measurements. In that case the tire forces can be determined to within 1% of actual values. The data processing technique is validated through examination of physical sensor measurements collected from a smart tire prototype during on-road vehicle testing. The network is shown to be capable of predicting the correct trends in the tire force and moment data in standard vehicle dynamics events, including pure slip and combined slip maneuvers.
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Citation
Bastiaan, J., "Physical Validation Testing of a Smart Tire Prototype for Estimation of Tire Forces," SAE Technical Paper 2018-01-1117, 2018, https://doi.org/10.4271/2018-01-1117.Data Sets - Support Documents
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