Simultaneous observation of the wheels’ torques and the vehicle dynamic state

Ouahi, Mohamed; Stéphant, Joanny; Meizel, Dominique

doi:10.1080/00423114.2013.768770

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…However, the tire lateral forces are not generated instantaneously. Thus the transient tire model is employed to further improve the accuracy of estimation, depicted by Equation (7).…”

Section: B Extended Kalman Filter Based Sensor Fusionmentioning

confidence: 99%

“…Nevertheless, wheel speeds could become unrelated to the vehicle velocity when driving in slippery conditions. In order to compensate the errors caused by wheel slip, GPS signal is used to correct the data of wheel speed sensor as a fusion of data [4][5][6][7]9]. Besides the longitudinal speed, sideslip angle is also an important parameter to describe vehicle dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Real-time estimation of vehicle's lateral dynamics at inclined road employing extended Kalman filter

Jiang

Victorino

Charara

2016

2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)

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In order to enhance road safety, this article presents a robust methodology for estimation of vehicle's lateral dynamics states, including velocities, sideslip angles, accelerations and tire forces. Awareness of vehicle dynamics states is essential for most intelligent vehicle control systems. However some of these states are not available by direct sensor measurement for technical and economical reasons. Even those available sensors are always coupled with noises or errors. In order to become reliable, many intelligent systems use very expensive sensors, which are not available for ordinary passenger cars. Therefore, it is quite interesting to have an accurate estimation of all dynamics states based on low-cost sensors. The main contribution of this article is providing a reliable estimator of vehicle dynamics states in presence of sensor noises and irregular roads. A best-wheel selection approach is applied in order to have a better understanding of vehicle's driving situation and enable us to select the best models for estimating dynamics states. The proposed estimator is based on the algorithm of extended Kalman Filter, which could minimize the variance of estimation errors. The estimator is implemented in our experimental vehicles and tested at banked track. Experimental results validate and prove the feasibility of this approach.

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“…However, the tire lateral forces are not generated instantaneously. Thus the transient tire model is employed to further improve the accuracy of estimation, depicted by Equation (7).…”

Section: B Extended Kalman Filter Based Sensor Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time estimation of vehicle's lateral dynamics at inclined road employing extended Kalman filter

Jiang

Victorino

Charara

2016

2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)

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“…In [8] Gadola et al implement a dynamic model based on a single-track vehicle model. In [6,9,15] different authors propose dynamic models based on double-track vehicle models.…”

Section: Introductionmentioning

confidence: 99%

Advances in Italian Mechanism Science

2019

Mechanisms and Machine Science

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The knowledge of the vehicle sideslip angle provides useful information about the state of the vehicle and it is often considered to increase the performance of the car as well as to develop safety systems, especially in the vehicle equipped with Torque Vectoring control systems. This paper describes two methods, based on the use of Kalman filters, to estimate the vehicle sideslip angle and the tire forces of a vehicle starting from the longitudinal and yaw velocity data. In particular, these data refer to on-track testing of a Range Rover Evoque performing ramp steer maneuvers at constant speed. The results of the sideslip estimation method are compared with the actual vehicle sideslip measured by a Datron sensor and are also used to estimate the tire lateral forces.

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“…The wheel ground forces, also called as vertical load, have a strong interaction with lateral and longitudinal dynamics [2]- [4]. A dramatic variation in the vertical load will probably cause a rollover situation, and will also cause a shift in tire's lateral and longitudinal forces [12].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Estimation of Vehicle Dynamics Through RLS and Kalman Filter Approaches

Jiang

Victorino²,

Charara³

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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This article presents a new methodology for estimation of vehicle's vertical forces in order to enhance road safety. Direct measurement of vertical forces requires a complex and expensive experimental setup , which is not acceptable for ordinary passenger cars. The main contribution of this article is providing a reliable estimator of vertical tire forces by using currently available low-cost sensors. The first advantage of the proposed method is that we modified the vehicle model to take into account the roll and pitch dynamics, which makes our estimator stay robust during sharp turning or at inclined road. The other advantage is that we proposed a process to identify the vehicle parameters, instead of regarding them as known constants. This could enable our estimator to stay reliable even when the parameters are wrongly configured. The parameter identification process is based on recursive least squares (RLS) algorithm. The state observers are based on Kalman filter. The estimation process is applied and compared to real experimental data obtained in real conditions. Experimental results validate and prove the feasibility of this approach.

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Simultaneous observation of the wheels’ torques and the vehicle dynamic state

Cited by 8 publications

References 22 publications

Real-time estimation of vehicle's lateral dynamics at inclined road employing extended Kalman filter

Real-time estimation of vehicle's lateral dynamics at inclined road employing extended Kalman filter

Advances in Italian Mechanism Science

Adaptive Estimation of Vehicle Dynamics Through RLS and Kalman Filter Approaches

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