A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations
AbstractThe possibility of using tires as active sensors opens the door to a huge number of different ways to accomplish this goal. In this case, based on a tire equipped with strain sensors, also known as an Intelligent Tire, relevant vehicle dynamics information can be provided. The purpose of this research is to improve the strain-based methodology for Intelligent Tires to estimate all tire forces, based only on deformations measured in the contact patch. Firstly, through an indoor test rig data, an algorithm has been developed to pick out the relevant features of strain data and correlate them with tire parameters. This information of the tire contact patch is then transmitted to a fuzzy logic system to estimate the tire parameters. To evaluate the reliability of the proposed estimator, the well-known simulation software CarSim has been used to back up the estimation results. The software CarSim has been used to provide the vehicle parameters in complex maneuvers. Finally, the estimations have been checked with the simulation results. This approach has enabled the behaviour of the intelligent tire to be tested for different maneuvers and velocities, providing key information about the tire parameters directly from the only contact that exists between the vehicle and the road. View Full-Text
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Mendoza-Petit, M.F.; Garcia-Pozuelo, D.; Diaz, V.; Olatunbosun, O. A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations. Sensors 2019, 19, 2973.
Mendoza-Petit MF, Garcia-Pozuelo D, Diaz V, Olatunbosun O. A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations. Sensors. 2019; 19(13):2973.Chicago/Turabian Style
Mendoza-Petit, Mª F.; Garcia-Pozuelo, Daniel; Diaz, Vicente; Olatunbosun, Oluremi. 2019. "A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations." Sensors 19, no. 13: 2973.
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