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Review

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

1
Department of Industrial and Mechanical Engineering, Automotive Group, University of Brescia, I-25123 Brescia, Italy
2
Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(3), 355; https://doi.org/10.3390/app8030355
Received: 8 January 2018 / Revised: 7 February 2018 / Accepted: 14 February 2018 / Published: 1 March 2018
(This article belongs to the Section Mechanical Engineering)
Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or “drifting” angle) is defined as the angle between the vehicle’s longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed. View Full-Text
Keywords: vehicle state estimation; vehicle dynamics; Extended Kalman Filter; Unscented Kalman Filter; GPS-aided estimation; neural networks vehicle state estimation; vehicle dynamics; Extended Kalman Filter; Unscented Kalman Filter; GPS-aided estimation; neural networks
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MDPI and ACS Style

Chindamo, D.; Lenzo, B.; Gadola, M. On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations. Appl. Sci. 2018, 8, 355. https://doi.org/10.3390/app8030355

AMA Style

Chindamo D, Lenzo B, Gadola M. On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations. Applied Sciences. 2018; 8(3):355. https://doi.org/10.3390/app8030355

Chicago/Turabian Style

Chindamo, Daniel, Basilio Lenzo, and Marco Gadola. 2018. "On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations" Applied Sciences 8, no. 3: 355. https://doi.org/10.3390/app8030355

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