An intelligent tyre based adaptive vehicle stability controller

Arat, Mustafa Ali; Singh, Kanwar Bharat; Taheri, Saied

doi:10.1504/ijvd.2014.060813

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…Hence, a complete description of the integrated vehicle state estimator as shown in Figure 2 is beyond the scope of this work. A thorough coverage of this topic with specific details about the different schemes used for tire/vehicle state and parameter estimation is included in our previous work , 2014Singh, 2012;. …”

Section: Estimation Of Vehicle States and Tire Forcesmentioning

confidence: 99%

Estimation of tire–road friction coefficient and its application in chassis control systems

Singh

Taheri

2014

Systems Science & Control Engineering

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113

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Knowledge of tire-road friction conditions is indispensable for many vehicle control systems. In particular, friction information can be used to enhance the performance of wheel slip control systems, for example, knowledge of the current maximum coefficient of friction would allow an anti-lock brake system (ABS) controller to start braking with the optimal brake pressure, meaning the early cycles of operation are more efficient, resulting in shorter stopping distances. Also, from a passive safety perspective, it may be useful to present the driver with friction information so they can adjust their driving style to the road conditions. Hence, it is highly desirable to estimate friction using existing onboard vehicle sensor information. Many approaches for estimating tire-road friction estimation have been proposed in the literature with different sensor requirements and relative excitation levels. This paper aims at estimating the tire-road friction coefficient by using a well-defined model of the tire behavior. The model adopted for this purpose is the physically based brush tire model. In its simplest formulation, the brush model describes the relationship between the tire force and the slip as a function of two parameters, namely, tire stiffness and the tire-road friction coefficient. Knowledge of the shape of the force-slip characteristics of the tire, possibly obtained through the estimation of both friction and tire stiffness using the brush model, provides information about the slip values at which maximum friction is obtained. This information could be used to generate a target slip set point value for controllers, such as an ABS or a traction control system. It is also important to realize that a model-based approach is inherently limited to providing road surface friction information when the tire is exposed to an excitation with high utilization levels (i.e. under high-slip conditions). To be of greatest use to active safety control systems, an estimation method needs to offer earlier knowledge of the limits. In order to achieve the aforementioned objective, an integrated approach using an intelligent tire-based friction estimator and the brush tire model-based estimator is presented. An integrated approach gives us the capability to reliably estimate friction for a wider range of excitations (both low-slip and high-slip conditions).

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Section: Estimation Of Vehicle States and Tire Forcesmentioning

confidence: 99%

Estimation of tire–road friction coefficient and its application in chassis control systems

Singh

Taheri

2014

Systems Science & Control Engineering

Self Cite

113

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“…The Smart Tire is expected to have capabilities to monitor in real-time the tire forces, road surface conditions, tire slip conditions, temperature, pressure and the tire tread depth ( Figure 1). Being able to leverage a plethora of information, a Smart Tire is expected to stimulate the development of advanced traction, braking and stability control systems for improving vehicle safety and performance [11][12][13][14].…”

Section: Figure 1: Smart Tire System Sensor Outputmentioning

confidence: 99%

Accelerometer Based Method for Tire Load and Slip Angle Estimation

Singh

Taheri

2019

Vibration

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Tire mounted sensors are emerging as a promising technology, capable of providing information about important tire states. This paper presents a survey of the state-of-the-art in the field of smart tire technology, with a special focus on the different signal processing techniques proposed by researchers to estimate the tire load and slip angle using tire mounted accelerometers. Next, details about the research activities undertaken as part of this study to develop a smart tire are presented. Finally, novel algorithms for estimating the tire load and slip angle are presented. Experimental results demonstrate the effectiveness of the proposed algorithms.

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“…Arat et al [20] examine such a method where a tireattached sensor unit (intelligent tire system) provides tire slip ratio information, tire forces, tire slip angle, and also the potential performance improvements offered by integrating this system with an adaptive vehicle stability controller. Lee et al [21] present a real-time maximum tire-road friction coefficient estimation method using the relationship between the wheel slip ratio and the friction coefficient.…”

Section: Introductionmentioning

confidence: 99%

Desenvolvimento de um modelo de dinâmica planar para veículos

Miranda¹,

Silva²,

Eckert³

et al. 2021

Blucher Engineering Proceedings

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Vehicle dynamics analyze the interactions among the vehicle, environment and driving style/profile applied. The tires are one of the most important components because all traction and braking forces are acting directly in the contact between the tire and ground. Therefore, this work focuses on the study of nonlinear Pacejka tire model, known as Magic Formula. The Pacejka equations are extensively applied in vehicle planar dynamics simulations. First, a vast bibliography review was performed aiming at the available tire models, considering its effects in the vehicle longitudinal and lateral dynamics. This paper presents the method to calculate the forces/efforts in planar behavior, necessary to determine torque, speed, power and others factors that can be used to design the vehicle control. The selected mathematical model was then implemented in Matlab/Simulink® environment, which enables an analysis of the slip and the tire forces according to the performed maneuvers. Finally, the resulting forces and vehicle dynamic response are compared, according to the variations of Pacejka parameters.

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An intelligent tyre based adaptive vehicle stability controller

Cited by 14 publications

References 17 publications

Estimation of tire–road friction coefficient and its application in chassis control systems

Estimation of tire–road friction coefficient and its application in chassis control systems

Accelerometer Based Method for Tire Load and Slip Angle Estimation

Desenvolvimento de um modelo de dinâmica planar para veículos

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