Estimation of Side Slip Angle Using Measured Tire Forces

Krantz, W. J.; Neubeck, Jens; Wiedemann, Jochen

doi:10.4271/2002-01-0969

Cited by 13 publications

(5 citation statements)

References 3 publications

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“…The accuracy of the estimation can be further enhanced with the additional multi-axis G sensor information [17]. There exist other approaches to estimate β from the indirect measurement of the tire forces [18]. Further detail of the β estimation algorithm is beyond the scope of this paper.…”

Section: Rollover Estimation Algorithmmentioning

confidence: 95%

Practical vehicle rollover avoidance control using energy method

Choi

2008

Vehicle System Dynamics

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In this paper, a novel rollover prevention control algorithm is developed for application on vehicles with a high centre of gravity. The developed algorithm can be implemented on any vehicle equipped with an electronic stability program with or without an extra roll rate sensor. The vehicle rollover index is defined from the vehicle lateral kinetic energy and the new concept of virtual gravity. The algorithm is implemented on a production hydraulic control unit and tested using a typical medium size sport utility vehicle up to a speed of 110 km h −1 . The test results show that the control algorithm prevents the vehicle rollover very successfully without any noticeable false activation or over correction resulting in severe under steer. Also, the controlled wheel speed shows a very stable and smooth trace.

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Section: Rollover Estimation Algorithmmentioning

confidence: 95%

Practical vehicle rollover avoidance control using energy method

Choi

2008

Vehicle System Dynamics

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“…where ' denotes the wheel base length and k denotes the dynamic index, which is about k % 1 for many standard passenger cars (Krantz et al, 2002) or slightly less, ie, k % 0.9, for more sportive cars (Blundell and Harty, 2004).…”

Section: Vehicle Modelmentioning

confidence: 99%

Kalman filter as a virtual sensor: applied to automotive stability systems

Wenzel

Burnham

Blundell

et al. 2007

Transactions of the Institute of Measurement and Control

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This paper demonstrates the use of an extended Kalman filter (KF) as a virtual sensor for non-measurable vehicle states and unknown vehicle parameters. The purpose of obtaining these values is to make them available within the control algorithms of the various automotive stability systems. Based on an extensive four-wheel vehicle model, an estimator is implemented on data from a test vehicle. Using available reference data, the suitability of the extended KF technique as a virtual sensor is demonstrated.

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“…The potential estimation of a vehicle body slip angle and yaw rate arising from on-line measurement of tire forces is evaluated in [20]. The conclusions, however, suggest that a vehicle fitted with tyre force sensors and no yaw rate sensor does not provide 'substantial benefit for vehicle state estimation in comparison to conventional system layouts'.…”

Section: Introductionmentioning

confidence: 97%