A Two-Stage Lyapunov-Based Estimator for Estimation of Vehicle Mass and Road Grade

McIntyre, Michael L.; Ghotikar, T.J.; Vahidi, Ardalan; Song, Xubin; Dawson, D.M.

doi:10.1109/tvt.2009.2014385

Cited by 91 publications

(51 citation statements)

References 20 publications

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“…The parameters in question could be properties of electrical devices [11]. The method of estimation could be Kalman Filtering, Least Square Error, Lyapunov Stability, Genetic Algorithms, and many others [1,13,14,17,21].…”

Section: Review Of Literaturementioning

confidence: 99%

“…An example for mass estimation employs the vehicle engine torque, drive train inertia, wind resistance, rolling resistance and road grade [7,13,21]. The problem as addressed by [21], is that the parameter estimation is highly sensitive to the estimation of the rolling resistance of the vehicle, a parameter which changes non-trivially over time.…”

Section: Review Of Literaturementioning

confidence: 99%

See 1 more Smart Citation

Real-Time Parameter Estimation Study for Inertia Properties of Ground Vehicles

Kolansky¹,

Sandu²

2013

Archive of Mechanical Engineering

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Vehicle parameters have a significant impact on handling, stability, and rollover propensity. This study demonstrates two methods that estimate the inertia values of a ground vehicle in real-time.Through the use of the Generalized Polynomial Chaos (gPC) technique for propagating the uncertainties, the uncertain vehicle model outputs a probability density function for each of the variables. These probability density functions (PDFs) can be used to estimate the values of the parameters through several statistical methods. The method used here is the Maximum A-Posteriori (MAP) estimate. The MAP estimate maximizes the distribution of P(β |z) where β is the vector of the PDFs of the parameters and z is the measurable sensor comparison.An alternative method is the application of an adaptive filtering method. The Kalman Filter is an example of an adaptive filter. This method, when blended with the gPC theory is capable at each time step of updating the PDFs of the parameter distributions. These PDF's have their median values shifted by the filter to approximate the actual values.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Real-Time Parameter Estimation Study for Inertia Properties of Ground Vehicles

Kolansky¹,

Sandu²

2013

Archive of Mechanical Engineering

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“…Hence, the algorithm is suitable for online applications. Finally, the parameter estimate X i+1 is obtained from (4).…”

Section: Incremental Total Least-squaresmentioning

confidence: 99%

“…However, this assumption may be invalid for energy-efficient driving strategies. To prevent the ambitious simultaneous estimation of time-varying road grade and constant vehicle mass, McIntyre et al [4] developed a two-stage estimation strategy. The vehicle mass is estimated by an adaptive least-squares (LS) scheme, followed by a nonlinear road grade estimator.…”

Section: Introductionmentioning

confidence: 99%

Vehicle mass estimation using a total least-squares approach

Rhode

Gauterin

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Abstract-We introduce an incremental total least-squares vehicle mass estimation algorithm, based on a vehicle longitudinal dynamics model. Available control area network signals are used as model inputs and output. In contrast to common vehicle mass estimation schemes, where noise is only considered at the model output, our algorithm uses an errors-in-variables formulation and considers noise at the model inputs as well. A robust outliner treatment is realized as batch total least-squares routine and hence, the proposed algorithm works in a superior way on a broad range of vehicle acceleration. The results of six test runs on various vehicle masses show highly accurate mass estimation results on high and low dynamics of vehicular operation.

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“…Many approaches use a combination of RLS and Kalman Filtering methods to simultaneously estimate road gradient and vehicle mass, including Raffone [8] and Vahidi [14]. Nonlinear observer structures are also used, by the current authors [9] [10] [15] [7] and by McIntyre [16] and Rajamani [17].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Simulation of Rapidly Changing Road Gradients

Wragge-Morley¹,

Herrmann²,

Burgess³

et al. 2016

SAE Int. J. Passeng. Cars - Mech. Syst.

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In vehicle dynamics modelling, the road profile is generally treated in one of two ways; either the gradient is a property that changes over a length scale far greater than that of the vehicle's wheelbase, or as a very detailed road surface model for determining the behaviour of vehicle suspensions. Occasionally, for modelling the behaviour of off-road vehicles, step-climbing manoeuvres are modelled. We propose an extension of these step-climbing models to a general, continuously varying road gradient model for cases where the distance over which the large gradient change occurs are of similar length-scale as the vehicle wheelbase. The motivation behind this work comes from a road gradient and vehicle mass estimation problem where it was noticed that very sudden gradient changes have a significant impact on the powertrain, but in a way that is not proportional to the attitude change of the vehicle. We present an analysis applicable to these cases where the road gradient change is substantially different from the vehicle attitude change. The analysis is built up from a very basic step climbing exercise to a general case solution for two dimensional longitudinal vehicle dynamics. Methods of handling road gradient and gradient change for modelling purposes are discussed. A method is proposed that defines the gradient change as an input to a backwards dynamic model that allows us to compute torque required to achieve a pre-defined datum speed during a gradient transition. The results of simulations for simple gradient change cases are shown. We discuss the implication of these road gradient transients for vehicle parameter estimation based on powertrain behaviour.

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A Two-Stage Lyapunov-Based Estimator for Estimation of Vehicle Mass and Road Grade

Cited by 91 publications

References 20 publications

Real-Time Parameter Estimation Study for Inertia Properties of Ground Vehicles

Real-Time Parameter Estimation Study for Inertia Properties of Ground Vehicles

Vehicle mass estimation using a total least-squares approach

Modelling and Simulation of Rapidly Changing Road Gradients

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