Estimation of the tire slip angle under various road conditions without tire–road information for vehicle stability control

Joa, Eunhyek; Yi, Kyongsu; Hyun, Y.

doi:10.1016/j.conengprac.2019.03.005

Cited by 33 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research on the stability region of a vehicle's sideslip angle phase plane trajectory has shown that a self‐stable region exists in the phase plane [25, 26]. However, studies have indicated that the speed of a vehicle, its road friction, and its front‐wheel angle affect its lateral stability [6, 10, 27]. Moreover, due to the high complexity and coupling of the vehicle dynamics system, the lateral stability of the vehicle is often the result of a combination of factors, rather than a single factor.…”

Section: Phase‐plane Stable‐region Determination Of Sideslip Anglementioning

confidence: 99%

Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Zhang

Guo

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

This study aimed to develop a coach state estimation and prediction system to enhance driving safety. Different from existing vehicle stability estimation studies, the authors propose a hybrid method to estimate and predict the state of a coach in real time. First, the vehicle sideslip angle and yaw rate are estimated by a three‐degrees‐of‐freedom vehicle model combined with an extended Kalman filter (EKF) estimation algorithm. Then, a steering system is established that replaces the front‐wheel angle with the steering wheel input torque. Next, a seven‐degrees‐of‐freedom vehicle model analyses the effects of various driving influencing factors on the vehicle sideslip angle and the boundary of the stable region of the phase plane of the vehicle sideslip angle rate, and a boundary value parameter database is obtained. A back propagation neural network (BPNN) model is then established to obtain the boundary function parameter values under multifactor coupling conditions. Furthermore, an online prediction of the steering wheel input torque in a time series is done, and the prediction value is input to the steering system and neural network model. The effectiveness of the proposed method was evaluated via simulations based on MATLAB/Simulink and TruckSim software.

show abstract

Section: Phase‐plane Stable‐region Determination Of Sideslip Anglementioning

confidence: 99%

Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Zhang

Guo

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

show abstract

“…Tires affect a vehicle handling, traction, ride comfort, and fuel consumption. To understand its importance, it is enough to remember that a vehicle can maneuver only by longitudinal, vertical, and lateral force systems generated under the tires [39]. A vehicle is made to move or change its direction in a specific way by forces acting through the tires.…”

Section: Figure 3 Forces On the Vehicle Body With The Aerodynamic Effect [34]mentioning

confidence: 99%

Survey of the Application Fields and Modeling Methods of Automotive Vehicle Dynamics Models

Szántó,

Hajdu,

Deák

2020

IJEMS

View full text Add to dashboard Cite

In this paper, a review is presented on automotive vehicle dynamics modeling. Applied vehicle dynamics models from various application fields are analyzed and classified in the first section. Vehicle dynamics models may be simplified because of different reasons: several control/estimation/analysis methods are suitable only for simplified models (e.g. using control-oriented models), or because of the computational cost. Detailed/truth models of vehicle dynamics represent another field of vehicle dynamics modeling, these models play an important role in the virtual prototyping of vehicles. In the second section, the main modeling considerations of vehicle dynamics are presented in longitudinal, lateral and vertical directions. Various physical effects must be considered in the case of vehicle dynamics modeling, a lot of these effects are significant only in a specific direction of the vehicle body, which is the main potential of model simplification. The section presents vehicle modeling considerations in all of the three translational directions of the vehicle body.

show abstract

“…The flow chart of the estimation algorithm is described as follows [31]: 1) UT transformation. Using a symmetrical sampling strategy, 2N+1 sigma point set is obtained near each estimation point.…”

Section: A Estimation Of Sideslip Anglementioning

confidence: 99%

Stability Research of Distributed Drive Electric Vehicle by Adaptive Direct Yaw Moment Control

et al. 2019

View full text Add to dashboard Cite

As one of the active safety technologies, stability control of vehicles has recently received great attention. In order to improve the handling stability of distributed drive electric vehicles under various extreme conditions, a direct yaw moment control (DYC) method based on a novel fuzzy sliding mode control (FSMC) is proposed. First, a linear 2DOF reference vehicle model as ideal value reference, a 7DOF vehicle model used for sideslip angle estimation, an electric-driving wheel model used to provide tire motion parameters based on CarSim platform are established. Then, FSMC is designed as the core decision-making layer of the control method to calculate the required additional yaw moment on the premise of estimating the sideslip angle. Four hub motors are allocated by the distribution method based on axle load proportion. Finally, under two typical working conditions, the four hub motors are allocated. Compared with traditional sliding mode control (SMC), the results show that FSMC can not only maintain vehicle stability more effectively under different working conditions, but also greatly reduce the occurrence of buffeting phenomenon, which has practical significance for engineering applications. INDEX TERMS Direct yaw moment control, fuzzy sliding mode control, lateral stability, active safety control strategy, distributed drive electric vehicle.

show abstract

Estimation of the tire slip angle under various road conditions without tire–road information for vehicle stability control

Cited by 33 publications

References 18 publications

Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Survey of the Application Fields and Modeling Methods of Automotive Vehicle Dynamics Models

Stability Research of Distributed Drive Electric Vehicle by Adaptive Direct Yaw Moment Control

Contact Info

Product

Resources

About