Vehicle State Estimation for Roll Control System

Ryu, Jeha; Moshchuk, Nikolai; Chen, Shih-Ken

doi:10.1109/acc.2007.4282456

Cited by 18 publications

(15 citation statements)

References 7 publications

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“…The calculated error terms in (6) can be used to correct the drift in the estimated roll angle due to noise and bias in the measured roll rate. The integration of the measured roll rate will provide accurate estimation during transient motion of the vehicle body.…”

Section: A Roll and Pitch Angle Estimation (Blocks B1 And B4)mentioning

confidence: 99%

“…In [22], authors used a combination of kinematics and model-based approaches with a linear tire model to estimate the vehicle side-slip angle. Some of these approaches such as [6], [8], [9], [10], [12], [18] have not been tested and verified with a real vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…For example, nonlinear observers designed in [1], [2], [3], [4] have been used to estimate longitudinal and lateral velocities. [5] and [6] used a limited-bandwidth integration technique. In [7], an algebraic approach based on numerical differentiation and diagnosis has been proposed to estimate both longitudinal and lateral velocities.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Vehicle Real-Time Longitudinal and Lateral Velocity Estimation

Rezaeian

Khajepour

Melek

et al. 2017

IEEE Trans. Veh. Technol.

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Accurate information about vehicle longitudinal and lateral velocities is vital for efficient operation of many vehicle control systems. In this paper, an estimation structure to simultaneously estimate vehicle velocities in longitudinal and lateral directions is developed and experimentally validated. This structure includes two parallel estimators: the first estimator is a kinematic-based observer for longitudinal velocity estimation, and the second is a combination of a kinematic-based observer and an inverse tire model to estimate vehicle lateral velocity. The proposed structure can effectively handle the additive biases, which are common in vehicle's stock accelerometers' signals, and provide accurate estimate of vehicle velocities when one (or more than one) wheel experiences the excessive slippage. Additionally, the proposed structure is not sensitive to changes in parameters of tire model and vehicle mass. The performance of this estimation structure is validated by experimental studies. A 0018-9545 (c)

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Section: A Roll and Pitch Angle Estimation (Blocks B1 And B4)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous Vehicle Real-Time Longitudinal and Lateral Velocity Estimation

Rezaeian

Khajepour

Melek

et al. 2017

IEEE Trans. Veh. Technol.

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“…In [190], the lateral dynamics model based roll angle estimator and the vehicle dynamics model based roll rate estimator are designed in order to identify the roll state of the vehicle by using some common sensor measurements such as the steering wheel angle, the lateral acceleration and the yaw rate. In [191], the roll angle and the roll rate estimators based on the two different vehicle models, 3-DOF (degree-of-freedom) and 1-DOF models, are evaluated and compared in both linear and non-linear regions using CarSim software. In [192][193][194], both vehicle body sideslip and roll angle are estimated by using global positioning system (GPS) and inertial measurements without knowing the vehicle model.…”

Section: Sideslip Angle and Roll Angle Estimationmentioning

confidence: 99%

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Cuma

Köroğlu

2015

Renewable and Sustainable Energy Reviews

294

119

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“…However, in case of the lateral vehicle velocity, side slip angle, and roll angle, it is practically impossible to measure the values due to sensor cost problems. For this reason, a variety of researches on these states estimation have been carried out [6]- [10]. In [6], lateral vehicle velocity estimation algorithm was proposed using vehicle model, RLS algorithm, and Extended Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors

Nam

Fujimoto

et al. 2011

Proceedings of the 2011 American Control Conference

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In this paper, new real-time methods for the lateral vehicle velocity and roll angle estimation are presented. Lateral tire forces, obtained from a multi-sensing hub (MSHub) unit, are used to estimate lateral vehicle velocity and a roll angle. In order to estimate lateral vehicle velocity, the recursive least square (RLS) algorithm is utilized based on a linear vehicle model and sensor measurements. In the roll angle estimation, the Kalman filter is designed for real-time estimation. The proposed estimation methods, RLS-based estimator and the Kalman filter, were verified by field tests on an experimental electric vehicle. Test results show that the proposed estimation methods provide better estimation performances and these methods are robust to road conditions. 978-1-4577-

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Vehicle State Estimation for Roll Control System

Cited by 18 publications

References 7 publications

Simultaneous Vehicle Real-Time Longitudinal and Lateral Velocity Estimation

Simultaneous Vehicle Real-Time Longitudinal and Lateral Velocity Estimation

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors

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