Vehicle Mass Estimator for Adaptive Roll Stability Control

Huh, Kunsoo; Lim, Soo-Jeong; Jung, Jin Seung; Hong, Daegun; Han, Sung Hee; Han, Kyutae; Jo, Hee Young; Yun, Jae Min

doi:10.4271/2007-01-0820

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…All the parameters in Table 1 are given in [6]. If the SVM method is chosen to identify the physical parameters of vehicle in the numerical simulation example, the free-decay responses of vehicle must be obtained first.…”

Section: A Numerical Simulation Examplementioning

confidence: 99%

“…Based on subspace identification method of modal parameters, Dong et al used a new parameter identification method for estimating roll and pitch moments of inertia of the on-road vehicle [5]. Huh et al designed a vehicle mass estimator for adaptive roll stability control [6]. Koulocheris et al applied a parametric identification method to estimate structural parameters of commercial passenger vehicle [7].…”

Section: Introductionmentioning

confidence: 99%

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A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

Zheng

Peng

Zhang

et al. 2015

Shock and Vibration

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A new physical parameter identification method for two-axis on-road vehicle is presented. The modal parameters of vehicle are identified by using the State Variable Method. To make it possible to determine the matricesM,C, andKof the vehicle, a known mass matrixΔMis designed to add into the vehicle in order to increase the number of equations ensuring that the number of equations is more than the one of unknowns. Therefore, the physical parameters of vehicle can be estimated by using the least square method. To validate the presented method, a numerical simulation example and an experiment example are given in this paper. The numerical simulation example shows that the largest of absolute value of percentage error is 1.493%. In the experiment example, a school bus is employed in study for the parameter identification. The simulation result from full-car model with the estimated physical parameters is compared with the test result. The agreement between the simulation and the test proves the effectiveness of the proposed estimation method.

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Section: A Numerical Simulation Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

Zheng

Peng

Zhang

et al. 2015

Shock and Vibration

View full text Add to dashboard Cite

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“…It also uses values obtained by previously developed and experimentally validated observers such as the mass of the vehicle m (it varies a lot in the case of the grape harvester when the grape tank is filled, e.g. see [7]), the mass distribution on the four wheels through two load transfer variables (LLT x and LLT y , see [8]) and the ground slope α calculated with the method used in [4].…”

Section: Longitudinal Stiffness Observationmentioning

confidence: 99%

Tire longitudinal grip estimation for improved safety of vehicles in off-road conditions

Nizard

Thuilot

Lenain³

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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We describe an on-line observer that allows to monitor the soil-tire contact longitudinal stiffness of off-road vehicles equipped with low cost sensors. The knowledge of such a value is a milestone in the development of active security devices, or at least a mean to inform the driver about the longitudinal stability of his vehicle. Indeed, these vehicles are frequently subject to ground changes and high slopes, causing fatal losses of control -slipping, rollover, etc. -every year. The observer has been tested and validated on a grape harvester in various conditions and its output evaluated with respect to an estimation based on expensive but precise sensors. These experimental results show that the driver can be informed in real-time about the longitudinal stability of his vehicle and alerted about critical situations.

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“…Vahidi [2] proposed a recursive least squares (RLS) technique for the estimation of mass and road grade. Huh et al [3] proposed vehicle mass estimation based on longitudinal, lateral dynamics and vertical dynamics of the model. Their strategy requires that the vehicle be equipped with active suspension which is available only in premium cars.…”

Section: Introductionmentioning

confidence: 99%

Vehicle parameter estimation using nested RLS algorithm

Kedar-Dongarkar

Das

2013

2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS)

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Energy consumption of a vehicle depends on the nature of road surface, grade and vehicle parameters. Predictive control strategies that rely on this information can benefit significantly from the knowledge of these parameters. This paper proposes an online estimation strategy to simultaneously estimate the vehicle mass, road frictional coefficient and wind velocity for a Series-Parallel Hybrid vehicle. A P2 hybrid vehicle longitudinal model is developed and used along with a two stage recursive least squares algorithm to estimate the dynamic parameters. The estimation strategy uses inputs from the vehicle longitudinal accelerometer sensor for determining road grade along with other powertrain signals.

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Vehicle Mass Estimator for Adaptive Roll Stability Control

Cited by 12 publications

References 10 publications

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

Tire longitudinal grip estimation for improved safety of vehicles in off-road conditions

Vehicle parameter estimation using nested RLS algorithm

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