Identification of vehicle parameters using modified least square method in ADAMS/Car

Bayani, Mohsen; Kazemi, Reza; Azadi, Shahram; Keshavarz, Arash

doi:10.1109/icmic.2011.5973683

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…For this purpose, using the same estimation approach, the values of the m, I z and L f parameters were estimated in the first seconds of car excitation (movement) and the results were used in the force estimator for the rest of the driving sequence. 17 Using the proposed parameter identifier ensures that the effects of parametric uncertainties are minimized.…”

Section: Results Of the Simulationsmentioning

confidence: 99%

Developing an analytical tyre model that estimates tyre forces using a least squares method

Bayani

Janbakhsh

Keshavarz

et al. 2012

Proceedings of the Institution of Mechanical Engineers, Part I:

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The use of control systems, especially vehicle dynamic control systems, is increasing at a remarkable rate. A key parameter that affects the performance of a vehicle dynamic control system is the interaction forces between the tyre and the road since these forces determine the acceleration, braking and steering properties of a car. Consequently, the accurate estimate of these forces is highly desired. Technical limitations and cost considerations mean that it is standard practice to estimate this data instead of performing measurements. Since available tyre models are generally an algebraic representation of the actual tyre and neglect variations in road conditions and uncertainties, an improved model for vehicle dynamic control applications is required. This paper proposes a tyre model which is formulated based on the least squares method and variable exponential forgetting estimation. The proposed estimator calculates tyre forces using information obtained from the sensors in a vehicle dynamic control system. The performance and robustness of the proposed tyre model is evaluated via virtual handling simulations in ADAMS software.

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Section: Results Of the Simulationsmentioning

confidence: 99%

Developing an analytical tyre model that estimates tyre forces using a least squares method

Bayani

Janbakhsh

Keshavarz

et al. 2012

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Han et al [6], Kim et al [7] and Fathy et al [8] among others have all used RLS methods to estimate vehicle mass, using various dynamics including lateral and roll dynamics. The extended RLS has been used to estimate a large number of vehicle parameters simultaneously in simulation by Bayani Khaknejad et al [9] and expanded to include a total least squares approach to a similar problem by Rhode and Gauterin [10].…”

Section: A Brief Overview Of the Current State Of The Art In Vehicle mentioning

confidence: 99%

Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car

Wragge-Morley¹,

Herrmann²,

Barber³

et al. 2015

SAE Int. J. Passeng. Cars – Electron. Electr. Syst.

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We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS). Therefore, a method using some filtering and supervisory logic is employed to give a sensible update behaviour for the vehicle mass estimation algorithm. The main function of the supervisor is to reject the mass estimate produced by unsuitable available data due to unmodelled loss forces. Good estimation results are obtained from data from a vehicle which was also fitted with some additional instrumentation including GPS sensors and a high quality IMU for scientific verification purposes.

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“…In recent years, the virtual prototyping technology(VPT) has became main research approach in aerodynamics and dynamics researches of automobile with the popularization of mechanical system simulation software and computational fluid dynamics(CFD) software. In this paper, the aerodynamic characteristics of racing car is taken into consideration which makes up the error in many scholar's researches regardless of aerodynamics [7,8] . Moreover, a feasible method to modeling the aerodynamic characteristics of rear wing in ADAMS is proposed in order to research on the performance of racing car with rear wing, it can cover the shortage of CFD experiment which can not predict the performance of the car with rear wing [9,10] .…”

Section: Introductionmentioning

confidence: 99%

Research on Crosswind Stability of FSAE Racing Car with Rear Wing at Different Attack Angles

Chen

Feng

et al. 2012

AMM

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In order to analyze the performance of a certain FSAE racing car with rear wing at different attack angles by virtual prototyping technology. The multi-body model of a FSAE racing car which takes non-linear factors into consideration was built by applying ADAMS/Car. The correctness of the model is verified by comparison with the actual experiment result. By the simulation of the air resistance and lift characteristics of the rear wing, a feasible method to building the aerodynamic characteristics of the rear wing in multi-body model was proposed. Based on these, the crosswind stability of FSAE racing car with rear wing at different attack angles was analyzed, the result shows that the effect of crosswind is reduced with the increase of the attack angle of the rear wing.

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Identification of vehicle parameters using modified least square method in ADAMS/Car

Cited by 8 publications

References 6 publications

Developing an analytical tyre model that estimates tyre forces using a least squares method

Developing an analytical tyre model that estimates tyre forces using a least squares method

Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car

Research on Crosswind Stability of FSAE Racing Car with Rear Wing at Different Attack Angles

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