An modular sideslip angle and road grade estimation scheme for four-wheel drive vehicles

Ma, Yingjun; Guo, Hongyan; Wang, Fei; Chen, Hong

doi:10.1109/chicc.2016.7554788

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…A plethora of observers can be used and an appropriate combination of a tire model and an enabling observer holds the key to accurate sideslip angle estimation. The commonly used observers include EKF [18, 42–58], unscented Kalman filter (UKF) [50, 55, 59–64], sliding mode observer (SMO) [42, 44, 45, 65, 66] and the forth [67–79]. In order to further improve estimation accuracy, the underlying vehicle parameters are simultaneously estimated in some studies.…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

“…The uncertain parameters were validated through experimental tests, which endows the linear tire model with higher reliability. In [43, 48, 59, 60, 68, 70, 71], the Dugoff tire model was employed to explicitly express the lateral and longitudinal tire forces as the functions of the tire slip angle, slip ratio, vertical load and tire–road friction coefficient. Like the Dugoff tire model, the Magic formula was also popularly employed as indicated in [47, 49, 51, 58, 61–63, 76, 84], which derives the lateral and longitudinal tire forces as the functions of several tire states.…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

“…Sideslip angle can be estimated in combination with numerous road states through co‐estimation schemes. In [57, 70, 82], road bank angle is estimated in combination with sideslip angle, which can improve the stability of observers in extreme manoeuvres. Similarly, road friction coefficient can also be online replenished to better describe the tire–road interactive forces [47, 67–69, 76, 87].…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

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Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially‐available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state‐of‐the‐art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics‐, dynamics‐ and neural network‐based estimators. A hardware‐in‐loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics‐based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics‐based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network‐based estimator can perform well in all manoeuvres once the training datasets are properly selected.

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Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

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Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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“…Some states of the vehicle motion can be measured directly by sensors, such as yaw rate and longitudinal/ lateral acceleration, and others need to be estimated by corresponding methods. The nonlinear full-order vehicle observer proposed in [33] is adopted in this paper to estimate velocity and sideslip angle.…”

Section: Experiments Analysismentioning

confidence: 99%

Steering Stability Control of a Four In-Wheel Motor Drive Electric Vehicle on a Road With Varying Adhesion Coefficient

Hou

Zhai

Sun³

et al. 2019

IEEE Access

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In order to improve the steering stability of a four in-wheel motor independent-drive electric vehicles (4MIDEVs) on a road with varying adhesion coefficient, such as on a joint road and a µ-split road, this paper presents a hierarchical electronic steering control (ESC) strategy. The upper level controller of the proposed ESC strategy achieves the adaptive control of the yaw rate and sideslip angle in the direct yaw-moment control based on the influence of road adhesion. The lower level controller is designed as a twohierarchy structure, which can adaptively change the torque allocation algorithm and achieve different weight controls of each wheel torque according to road adhesion coefficient. The results of real-time simulation conducted in the RT-LAB testing platform and a real-car test indicate an improvement in the steering stability of the 4MIDEV on a road with varying adhesion coefficient. Particularly the ''double lane change'' testing carried out on a joint road and a µ-split road shows a yaw rate error reduction of up to 55.1%, compared with the ordinary control strategy, with output torque and its fluctuation of each in-wheel motor significantly reduced. INDEX TERMS Electric vehicles, independent-drive, stability, adaptive control, adhesion coefficient.

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