Adaptive Scheme for the Real-Time Estimation of Tire-Road Friction Coefficient and Vehicle Velocity

Han, Kyoungseok; Lee, Eunjae; Choi, Mooryong; Choi, Seibum B.

doi:10.1109/tmech.2017.2704606

Cited by 73 publications

(21 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e vehicle dynamics response approaches try to estimate the friction using mathematical models. e most commonly mathematical models are wheel dynamic model [11,12], roll dynamic model [13], quarter car model [14], four-wheel vehicle model [3], bicycle model [15,16], and tire model such as Pacejka tire model, Dugoff tire model, and Burckhardt tire model [17]. e vehicle dynamics response approaches do not require additional sensors and the accuracy of the recognition is within the allowable range.…”

Section: Review Of Previous Resultsmentioning

confidence: 99%

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Zhang

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The accurate recognition of road condition is one of the important factors that influence vehicle safety performance. This paper comes up with an original mathematical method of an interval recognition algorithm of the pavement surface condition based on Lagrange interpolation. The ordinate of the peak point is solved by the Lagrange interpolation method, and the pavement surface condition is deduced by the interval identification algorithm. The simulation results from six typical roads and the varied pavement surface show that besides the cobblestone pavement which is not common in the daily road, the estimation error of the initial tire-road friction coefficient by the Lagrange interpolation method is less than 2%, the pavement surface condition can be identified by interval recognition algorithm quickly and accurately, and the response time is less than 0.2 seconds.

show abstract

Section: Review Of Previous Resultsmentioning

confidence: 99%

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Zhang

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…This constraint is specified in our optimal control problem. In short, the defined models in (9) and (12) are enforced to the region where α f < α max f and |α f | > |α r |, and the other regions are not in the scope of this paper.…”

Section: B Vehicle Handling Characteristicsmentioning

confidence: 99%

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Han

Park

Sankar³

et al. 2021

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control methods. For safety's sake, most production vehicles are built to exhibit an understeer handling characteristics to some extent. By monitoring how much the vehicle is biased into the understeer state, the controller attempts to adjust this amount in a way that improves the vehicle cornering performance. With this particular strategy, an innovative controller can be designed without road friction information, which complicates the conventional control methods. In addition, unlike the conventional controllers, the reference yaw rate that is highly dependent on road friction need not be defined due to the proposed control structure. The optimal control problem is formulated in a model predictive control framework to handle the constraints efficiently, and simulations in various test scenarios illustrate the effectiveness of the proposed approach. Index Terms-Model Predictive Control, Constrained Control, Vehicle Handing Characteristics, and Cornering Performance. Seibum B. Choi(M'09) received the B.S. degree in mechanical engineering from Seoul National University, Seoul, Korea, in 1985, the M.S.

show abstract

“…An adaptive observer is designed in ref. for estimation of the tire‐road friction condition based on a recursive least squares algorithm, without adding any extra sensor on a production vehicle. The front biased braking characteristic is also studied and longitudinal vehicle velocity is identified during the tire‐road friction estimation process.…”

Section: Tire Models and Force Estimationmentioning

confidence: 99%

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

Argani

Hashemi

Khajepour

et al. 2018

Adv Materials Technologies

View full text Add to dashboard Cite

With the prospect of autonomous driving on roads in near future, it is paramount to make the vehicles safe on any driving and road condition. This is only possible by additional sensors to make up for the driver's cognitive and sensory system. Measuring road condition and tire forces especially in autonomous vehicles are vital in their safety, reliability, and public confidence in automated driving. Real time measurement of road condition and tire forces in buses and trucks can significantly improve the safety of road transportation system, and in miming/construction and off-road vehicles can improve performance, tire life, and reduce operational costs.There are three particular branches of research in the area of tire condition monitoring systems (TCMS) including estimation, sensing, and power harvesting. The main focus of this review article would be on progress in estimation techniques, energy harvesting, and also sensing approaches for TCMS based on piezoelectric, electromagnetic, and triboelectric nanogenerators (TENGs). Indeed, the fusion of these three areas in a singular package leads to designing future intelligent tires. The implementation of intelligent tires is highly advantageous for reduction of disastrous accidents,CO 2 , and noise emissions, and also fuel consumption. Figure 1 depicts an intelligent tire with its basic parts, its crucial applications, and advantages. In Figure 1, NGs, PEGs, and EMGs refer to nanogenerators, piezoelectric nanogenerators, and electromagnetic generators, respectively.Advances in applications of sensor technologies, sensor fusion, and cooperative estimation in intelligent transportation systems facilitate having reliable tire forces. Consequently, developing a flexible estimation structure to operate the available sensor data in production vehicles is a preeminent objective of the car manufacturers. In this direction, reliable estimation of tire forces at a reasonable cost is necessary for proper functioning of active safety systems in current vehicles. Longitudinal, lateral, and vertical tire forces make major contributions to velocity estimation, [3] traction and stability control, [4,5] and obstacle avoidance systems. [6,7] This article provides a comprehensive review on intelligent tire and TCMS. The first part of the article describes tire models, techniques for tire force, and tire-road friction The quest to utilize intelligent tires has prompted substantial multidisciplinary research including vehicle dynamics, control, estimation, energy harvesting, and even nanotechnology. This review article presents the progress in the area of tire condition monitoring systems (TCMS) and intelligent tires using devices fabricated based on piezoelectric, electromagnetic, and triboelectric effects. Three main branches of the research in this area are presented, including estimation techniques, sensing, and energy harvesting approaches. The authors delineate the importance of TCMS for vehicle active safety systems, its importance for transportation safety, and also its remarka...

show abstract

Adaptive Scheme for the Real-Time Estimation of Tire-Road Friction Coefficient and Vehicle Velocity

Cited by 73 publications

References 32 publications

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Interval Recognition Algorithm of the Pavement Surface Condition Based on Lagrange Interpolation Method

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

Contact Info

Product

Resources

About