Estimation of tyre forces using smart tyre sensors and artificial intelligence

Bastiaan, Jennifer

doi:10.1504/ijvd.2018.096107

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Peak positive (i.e., tensile) strain levels, as predicted by the model, are around 6000 µϵ, or about 6%. Typically, such high strain levels would be expected in the sidewall region of the tire, which is less stiff than the tread region [33]. Therefore, the tire FEA model as developed should be used to investigate trends in longitudinal strain data in the tire tread area, rather than predict absolute values.…”

Section: Virtual Strain Data Extractionmentioning

confidence: 99%

Intelligent Tire Prototype in Longitudinal Slip Operating Conditions

Bastiaan,

Chawan,

Eum

et al. 2024

Sensors

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With the recent advances in autonomous vehicles, there is an increasing need for sensors that can help monitor tire–road conditions and the forces that are applied to the tire. The footprint area of a tire that makes direct contact with the road surface, known as the contact patch, is a key parameter for determining a vehicle’s effectiveness in accelerating, braking, and steering at various velocities. Road unevenness from features such as potholes and cracks results in large fluctuations in the contact patch surface area. Such conditions can eventually require the driver to perform driving maneuvers unorthodox to normal traffic patterns, such as excessive pedal depressions or large steering inputs, which can escalate to hazards such as the loss of control or impact. The integration of sensors into the inner liner of a tire has proven to be a promising method for extracting real-time tire-to-road contact patch interface data. In this research, a tire model is developed using Abaqus/CAE and analyzed using Abaqus/Explicit to study the nonlinear behavior of a rolling tire. Strain variations are investigated at the contact patch in three major longitudinal slip driving scenarios, including acceleration, braking, and free-rolling. Multiple vertical loading conditions on the tire are applied and studied. An intelligent tire prototype called KU-iTire is developed and tested to validate the strain results obtained from the simulations. Similar operating and loading conditions are applied to the physical prototype and the simulation model such that valid comparisons can be made. The experimental investigation focuses on the effectiveness of providing usable and reliable tire-to-road contact patch strain variation data under several longitudinal slip operating conditions. In this research, a correlation between FEA and experimental testing was observed between strain shape for free-rolling, acceleration, and braking conditions. A relationship between peak longitudinal strain and vertical load in free-rolling driving conditions was also observed and a correlation was observed between FEA and physical testing.

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Section: Virtual Strain Data Extractionmentioning

confidence: 99%

Intelligent Tire Prototype in Longitudinal Slip Operating Conditions

Bastiaan,

Chawan,

Eum

et al. 2024

Sensors

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“…The model was able to predict vertical forces with sufficient accuracy, but had issues with longitudinal forces. Finally, in [12], radial basis function networks were trained on pure slip conditions, but tested on combined slip, which provided results within 1% accuracy.…”

Section: Contributors and Funding Sourcesmentioning

confidence: 99%

Nonlinear Control of a Ground Vehicle using Data-Driven Dynamic Models

Hassen

Chour

Weaver

et al. 2020

SAE Technical Paper Series

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As autonomous vehicles continue to grow in popularity, it is imperative for engineers to gain greater understanding of vehicle modeling and controls under different situations. Most research has been conducted on on-road ground vehicles, yet off-road ground vehicles which also serve vital roles in society have not enjoyed the same attention. The dynamics for off-road vehicles are far more complex due to different terrain conditions and 3D motion. Thus, modeling for control applications is difficult. A potential solution may be the incorporation of empirical data for modeling purposes, which is inspired by recent machine learning advances, but requires less computation. This thesis presents results for empirical modeling of an off-road ground vehicle, Polaris XP 900. As a first step, data was collected for 2D planar motion by obtaining several velocity step responses. Multivariable polynomial surface fits were performed for the step responses. Sliding mode control layered on top of pure pursuit guidance is then used to drive the vehicle for waypoint following, using the empirical model. Simulation and experimental results show that the vehicle can perform waypoint following for a circular and sinusoidal with minimal error. Furthermore, more experimental data was collected to show the effects of adaptive velocity and adaptive lookahead for path tracking. A comparison of the controller's performance was also explored between on-road and off-road terrain.

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