Identifying Vehicle Model Parameters Using Measured Terrain Excitations

Müller, T. L.; Ferris, Joseph E.; Detweiler, Zachary R.; Smith, Hurtford

doi:10.4271/2009-01-1197

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…and δv n (t) is represented by (15)(16) in Section 4.1. An interesting and key note is that, upon dividing through by m , we obtain a simple time varying parameter 1st order ordinary differential equation which we can solve directly for the sensitivity δv(t) m :…”

Section: Sensitivity Of Velocity With Respect To Vehicle Massmentioning

confidence: 99%

“…The SAE testing standard for longitudinal vehicle chassis parameter estimation outlines several basic experimental conditions which facilitate estimation accuracy [14]. Within another context, work by Muller et al demonstrates that the addition of speed bumps to a road test allows relevant suspension parameters to be estimated with greater precision [15]. While such work effectively discusses some limitations of vehicle chassis parameter identifiability, there are still many relevant questions which remain unanswered.…”

Section: Introductionmentioning

confidence: 99%

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Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

Kandel,

Wahba,

Fathy

2020

Preprint

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This paper examines the impact of mean-square terrain variability on longitudinal chassis parameter identifiability. This analysis is motivated by the immediate value of effective parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse computational and on-road conditions. While the limitations of such algorithms' accuracy with respect to some driving conditions have been studied, their dependence on road grade variability remains largely unexplored. We address this open question by presenting two key contributions. First, this paper presents analytic derivations of the Fisher information matrix associated with estimating mass, drag, and rolling resistance parameters from longitudinal dynamics. We validate the analytic sensitivity expressions using simulations and experimental data gathered from an instrumented Volvo VNL300 heavy-duty freight truck. Then, this paper presents Monte Carlo simulations which illustrate the average improvements in chassis parameter identifiability associated with drive-cycles characterized by higher mean-square road grade. Our simulation studies demonstrate this result under a variety of drive cycles.

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Section: Sensitivity Of Velocity With Respect To Vehicle Massmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

Kandel,

Wahba,

Fathy

2020

Preprint

View full text Add to dashboard Cite

show abstract

Interpolation methods for high-fidelity three-dimensional terrain surfaces

Detweiler

Ferris

2010

Journal of Terramechanics

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Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

Kandel

Wahba

Fathy

2021

ASME Letters in Dynamic Systems and Control

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This paper investigates the theoretical Cram´er-Rao bounds on estimation accuracy of longitudinal vehicle dynamics parameters. This analysis is motivated by the value of parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse conditions. While the implementation of such algorithms has been studied, the question of fundamental limits on their accuracy remains largely unexplored. We address this question by presenting two contributions. First, this paper presents theoretical findings which reveal the prevailing effects underpinning vehicle chassis parameter identifiability. We then validate these findings with data from on-road experiments. Our results demonstrate, among a variety of effects, the strong relevance of road grade variability in determining parameter identifiability from a drive cycle. These findings can motivate improved experimental designs in the future.

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Identifying Vehicle Model Parameters Using Measured Terrain Excitations

Cited by 8 publications

References 5 publications

Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

Interpolation methods for high-fidelity three-dimensional terrain surfaces

Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

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