[논문]Estimation of Tire Load and Vehicle Parameters Using Intelligent Tires Combined With Vehicle Dynamics

Jeong, Dasol; Kim, Seungtaek; Lee, Jonghyup; Choi, Seibum B.; Kim, Mintae; Lee, Hojong

doi:10.1109/tim.2020.3031124

[해외논문] Estimation of Tire Load and Vehicle Parameters Using Intelligent Tires Combined With Vehicle Dynamics

IEEE transactions on instrumentation and measurement, v.70, 2021년, pp.1 - 12

Jeong, Dasol (Korea Advanced Institute of Science and Technology, Daejeon, South Korea) , Kim, Seungtaek (Korea Advanced Institute of Science and Technology, Daejeon, South Korea) , Lee, Jonghyup (Korea Advanced Institute of Science and Technology, Daejeon, South Korea) , Choi, Seibum B. (Korea Advanced Institute of Science and Technology, Daejeon, South Korea) , Kim, Mintae (Hankook Tire Co. Ltd., Daejeon, South Korea) , Lee, Hojong (Hankook Tire Co. Ltd., Daejeon, South Korea)

Abstract ▼ AI-Helper

This article focuses on the estimation of static/ dynamic tire load and vehicle parameters using intelligent tires and vehicle dynamics. This study is conducted to improve and ensure the performance of advanced vehicle control using accurate vehicle and tire states. The contact angle between tire and road surface is calculated by an intelligent tire and is used for tire load estimation. The tire load estimation results are validated by the flexible ring tire model. For a fast sampling rate and high robustness, a new estimation algorithm, which combines intelligent tire and vehicle dynamics, is proposed in this article. Not only the tire load but also the vehicle parameters, such as total mass, center of gravity point (CG point), and center of gravity height (CG height), are estimated by the proposed estimation algorithm. The proposed estimation algorithm is verified by an indoor test using Flac trac (tire test system) and a real-time test using AutoBox III. In short, the estimation algorithm proposed in this article can estimate static/dynamic tire load and vehicle parameters with a fast sampling rate and high robustness.

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DOI : 10.1109/TIM.2020.3031124
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