Road slope and vehicle dynamics estimation

Sebsadji, Yazid; Glaser, Sébastien; Mammar, Saïd; Dakhlallah, Jamil

doi:10.1109/acc.2008.4587221

Cited by 59 publications

(26 citation statements)

References 6 publications

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“…e) ESC observer: As the longitudinal and lateral vehicle behavior are highly non-linear, the observer is based on an extended Kalman filter, like in [9]. The figure 6 shows the results of the implemented ESC during a emergency avoidance maneuver on the highway.…”

Section: Electronic Stability Controller (Esc)mentioning

confidence: 99%

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Development and assessment of the cooperation between drive line and stability control

Chretien¹,

Holzmann²,

Glasser³

et al. 2010

Proceedings of the 2010 American Control Conference

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This article presents the outcomes of the virtual prototyping in advanced phase to develop and validates the introduction of dynamics control into a conventional passenger car. Thus after having presented the basic vehicle model, the additional components and the different strategies like ABS, TCS and ESP are emphasized. Drawbacks of multiple actuators are the difficulties to manage the variants in the vehicle and its control for the assistant systems that have to deliver the appropriate commands. Therefore a vehicle longitudinal controller will be presented at the end of this article.

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Section: Electronic Stability Controller (Esc)mentioning

confidence: 99%

“…Thus the most used methods are to compute the speed according to a hybrid model based estimation or through an observer. Here, we used an observer, composed of a linear Kalman filter and a computation module, like in [9].…”

Section: Stability Functionsmentioning

confidence: 99%

Development and assessment of the cooperation between drive line and stability control

Chretien¹,

Holzmann²,

Glasser³

et al. 2010

Proceedings of the 2010 American Control Conference

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“…With linearization, a suboptimal estimation method can be applied to the EKF. For the EKF theory, equation of state (15) and equation of observation (16) are representable as…”

Section: Ekf Algorithmmentioning

confidence: 99%

“…In [14], combined with a nonlinear tire dynamics model, the EKF algorithm was used to estimate the sideslip angle. In [15], EKF was adopted to estimate longitudinal force and road friction coefficient of tires from the longitudinal/lateral velocity and yaw rate. In addition, the road-slope and vehicleslip angle were obtained.…”

Section: Introductionmentioning

confidence: 99%

State Estimation of Vehicle’s Dynamic Stability Based on the Nonlinear Kalman Filter

Pei

Liu

et al. 2018

Automot. Innov.

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An accurate estimation of a vehicle's state of motion is the basis of dynamic stability control. Two different nonlinear Kalman filters are adopted for the estimation of the vehicle's lateral/rollover stability state. First, the overall structure of the state estimation with four inputs and four outputs is introduced. After determining tire-cornering stiffness using a recursive leastsquares (RLS) method, the equations of state and of observation for the nonlinear Kalman filter are established based on a vehicle model with four degrees of freedom including planar and rollover dynamics. Then, the specific steps of real-time state estimation using the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are both given. In a co-simulation, we find that the RLS algorithm estimates tire-cornering stiffness accurately and quickly, and the UKF improves the effect of state estimation compared with EKF. In addition, the UKF is verified against data from vehicle tests. The results show the proposed method is reliable and practical in estimating vehicle states.

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“…Estimation of the road slope for various applications is also treated in e.g. Lingman and Schmidtbauer (2002), Sebsadji et al (2008) and Bae et al Vinnova industrial excellence center LINK-SIC and Scania CV have supported this work.…”

Section: Introductionmentioning

confidence: 99%

Road Slope Analysis and Filtering for Driveline Shuffle Simulation

Myklebust

Eriksson

2012

IFAC Proceedings Volumes

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In powertrain analysis, simulation of driveline models are standard tools, where efficient and accurate simulations are important features of the models. One input signal with high impact on the accuracy is the road slope. Here it is found that the amplitude discretization in production road-slope sensors can excite vehicle shuffle dynamics in the model, which is not present in the real vehicle. To overcome this problem road-slope information is analyzed with the aid of both measured and synthetic road profiles, where the latter are generated from regulatory road specifications. The analysis shows that it is possible to separate vehicle shuffle resonances and road-slope information, and designs are proposed for on-and off-line filtering of the road-slope-sensor signal in spatial coordinates. Applying the filter to measured data shows that vehicle shuffle is significantly attenuated, while the shape of the road slope profile is maintained. As a byproduct the use of smoothing the rolling resistance is shown.

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Road slope and vehicle dynamics estimation

Cited by 59 publications

References 6 publications

Development and assessment of the cooperation between drive line and stability control

Development and assessment of the cooperation between drive line and stability control

State Estimation of Vehicle’s Dynamic Stability Based on the Nonlinear Kalman Filter

Road Slope Analysis and Filtering for Driveline Shuffle Simulation

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