Enhancing brush tyre model accuracy through friction measurements

O'Neill, A; Prins, Jan F.; Watts, John F.; Gruber, Patrick

doi:10.1080/00423114.2021.1893766

Cited by 15 publications

(4 citation statements)

References 21 publications

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“…In 2021, Alexander et al constructed a model that expressed the discrete friction coefficient µ by replacing it with the actual friction characteristics that depend on the tread temperature and sliding speed. By combining the above model and a thermal model as a substitute for the experimental temperature data, they obtained results that agreed with the MF, realized with measurements obtained using the test machine [25].…”

Section: Tire Characteristic Modelssupporting

confidence: 71%

Study on the Influence of Environmental Conditions on Road Friction Characteristics

et al. 2023

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This study focuses on changes in the friction characteristics of paved roads under various conditions from the viewpoint of traffic safety. In general, the braking characteristics of road vehicles are examined using the μ–s characteristics of the tires. Therefore, in our research, we used three types of limited datasets and identified them using the Magic Formula proposed by Prof. Pacejka. Based on various experiments, it was shown that changes in the road surface environment, such as dry and wet conditions, significantly affect the μ–s characteristics, and this influence varies significantly depending on the pavement conditions. In this study, as the first stage of the analysis of these influences, it was clarified that the difference in the friction characteristics of wet and dry road surfaces varies significantly depending on the pavement surface, which is based on experimental results obtained using actual roads. As this variation is closely related to the safety of actual road traffic, we used a brush model, which is a dynamic model of the road surface and tires, to clarify the differences in this characteristic.

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Section: Tire Characteristic Modelssupporting

confidence: 71%

Study on the Influence of Environmental Conditions on Road Friction Characteristics

et al. 2023

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“…In general, both quantitites may be made dependent explicitly on the vector position x or on the nondimensional sliding speed v s ðx; sÞ; however, to keep the complexity of the analysis within acceptable levels, this paper only uses a constant values for l, 8 under the assumption of Amontons-Coulomb friction. For more sophisticated formulations the reader may instead refer to [49][50][51][52][53][54].…”

Section: List Of Symbols Forces and Moments Q Tmentioning

confidence: 99%

Analytical results in transient brush tyre models: theory for large camber angles and classic solutions with limited friction

et al. 2021

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This paper establishes new analytical results in the mathematical theory of brush tyre models. In the first part, the exact problem which considers large camber angles is analysed from the perspective of linear dynamical systems. Under the assumption of vanishing sliding, the most salient properties of the model are discussed with some insights on concepts as existence and uniqueness of the solution. A comparison against the classic steady-state theory suggests that the latter represents a very good approximation even in case of large camber angles. Furthermore, in respect to the classic theory, the more general situation of limited friction is explored. It is demonstrated that, in transient conditions, exact sliding solutions can be determined for all the one-dimensional problems. For the case of pure lateral slip, the investigation is conducted under the assumption of a strictly concave pressure distribution in the rolling direction.

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“…We use the nonlinear Brush tire model to describe the tire force. Compared with the MF tire model, the Brush tire model needs fewer parameters to fit [29]. The joint model definition of longitudinal and lateral forces is as follows:…”

Section: B Brush Tire Modelmentioning

confidence: 99%

Enhanced Tire Road Friction Coefficient Estimation Through Interacting Multiple Model Design based on Tire Force Observation

Wan,

Sheng,

Cai

et al. 2024

Preprint

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Accurately understanding the Tire-Road Friction Coefficient (TRFC) interaction is crucial for enhancing overall vehicle control performance. However, existing estimation methods heavily rely on the accuracy of tire modeling, which may not predict TRFC effectively under poor modeling accuracy or changing operating conditions. This paper proposes an interacting multiple-model TRFC estimation method using tire force observation. Firstly, taking advantage of the observation capabilities of distributed drive electric vehicles, a longitudinal force estimator with unknown inputs is designed. Simultaneously, a lateral force observer based on adaptive sliding mode observer (ASMO) is developed, fully utilizing the onboard sensor data of the vehicle. A TRFC estimator with square root cubature Kalman filter, incorporating square root filtering, is proposed to reduce algorithm complexity while ensuring accuracy. Finally, an interacting multiple-model mechanism is specifically developed for both pure longitudinal dynamics and combined conditions. Through Carsim-Matlab co-simulation and high TRFC test conditions of a real vehicle equipped with a 6-axis wheel transducer, it is shown that the algorithm proposed in this article can accurately estimate tire force and TRFC under various maneuvering conditions and different TRFC conditions. Based on the comparison with traditional algorithms, it shows that our proposed algorithm has higher estimation accuracy and robustness.

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Enhancing brush tyre model accuracy through friction measurements

Cited by 15 publications

References 21 publications

Study on the Influence of Environmental Conditions on Road Friction Characteristics

Study on the Influence of Environmental Conditions on Road Friction Characteristics

Analytical results in transient brush tyre models: theory for large camber angles and classic solutions with limited friction

Enhanced Tire Road Friction Coefficient Estimation Through Interacting Multiple Model Design based on Tire Force Observation

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