Design of a Smart Tire Sensor System
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A research project is conducted that involves the design of a smart tire sensor system that can determine six tire outputs, including tire longitudinal force, tire lateral force, tire vertical force, tire aligning moment, tire / road friction coefficient and tire air inflation pressure. All of these quantities are estimated using in-tire deformation sensors. The rationale for conducting the smart tire research project is that its results have the potential to improve ground vehicle safety. The objectives of the research project are to identify the quantity and types of sensors required, determine the sensor locations and orientations in the tire, develop post-processing methods for the raw sensor output and confirm correct operation of the sensor system, which involves prototyping and physical testing. Strain is predicted in the tire inner liner as part of a tire finite element analysis study. The tire finite element model is used to calculate strain (inputs) and tire forces (outputs) for use in artificial neural networks. Results from the radial basis function networks studied are excellent, with calculated tire forces within 1% and tire aligning moment within 1%. The conclusion is that radial basis function networks can likely be used effectively for analysis of strain sensor measurements in the smart tire sensor system. Further studies using virtual strain show that the system should have two in-tire strain sensors located near one another at the outside sidewall, with one oriented longitudinally and the other oriented radially, along with an angular position sensor. Commercially available piezoelectric deformation sensors are installed in this layout, along with a rotary encoder, in a smart tire physical prototype. On-road data collected during physical testing are used with radial basis function neural networks to estimate the three orthogonal tire forces and the tire aligning moment. The networks are found capable of predicting the correct trends in the tire force data over several testing events. While the smart tire sensor system in its current state of development is not production-ready, the research project has resulted in new scientific knowledge that will be the foundation of future smart tire work. Contributions include the identification of in-tire sensor quantity, locations and orientations, confirmation that an angular position measurement is necessary and the determination of the artificial neural network architecture. The most significant remaining smart tire technical hurdle is the identification of a sufficiently durable strain sensor for in-tire use. If this strain sensor can be found, the next steps will include validation of the non-force tire estimates and studies of temperature effects, wireless data transmission and energy harvesting for a battery free design. Despite these outstanding concerns, the results of the smart tire research project show that the concept is feasible and further work is justified.