Road profile input estimation in vehicle dynamics simulation

Imine, Hocine; Delanne, Y; M'Sirdi, Kouider

doi:10.1080/00423110500333840

Cited by 97 publications

(62 citation statements)

References 4 publications

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“…This method provides an accurate, high resolution measurement of road profile, though the associated costs of laser-based technology are a disadvantage. Imine et al [6] Fair agreement is found for the proposed method, with noticeable local discrepancies in the estimated profile.…”

Section: Introductionmentioning

confidence: 59%

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Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique

Harris

González

O’Brien

et al. 2010

Journal of Sound and Vibration

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Pavement surface profiles induce dynamic ride responses in vehicles which can potentially be used to classify road surface roughness. A novel method is proposed for the characterisation of pavement roughness through an analysis of vehicle accelerations.A combinatorial optimisation technique is applied to the determination of pavement profile heights based on measured accelerations at and above the vehicle axle. Such an approach, using low-cost inertial sensors, would provide an inexpensive alternative to the costly laser based profile measurement vehicles. The concept is numerically validated using a half-car roll dynamic model to infer measurements of road profiles in both the left and right wheel paths.

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Section: Introductionmentioning

confidence: 59%

“…Each characterised road profile has an IRI value within  2 % of those for the true profiles. This indicates that in theory, the method proposed has the potential to measure road roughness to a class I standard [6].…”

Section: Iri Ratings For Characterised Profilesmentioning

confidence: 99%

Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique

Harris

González

O’Brien

et al. 2010

Journal of Sound and Vibration

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“…Finally, these vehicle equations can be combined with the equations of the infrastructure model under investigation to analyse vehicle-infrastructure problems, i.e., impact factor due to traffic in roads and bridges [6][7][8][9][10][11][12][13][14][15][16][17]32], dynamics in railway bridges [2,3], pavement deterioration due to the passage of heavy vehicles [33][34], performance of vehicle elements such as suspensions or tyres [1,[36][37][38][39], evaluation of ride quality and pavement unevenness [40][41][42], or weigh-in-motion applications [43][44][45] amongst others. In the case of simulating the interaction between a vehicle and a bridge, Lagrange multipliers [17], dynamic condensation [3] or iterative procedures [31] are some of the most popular approaches to combine the equations of motion of both models.…”

Section: Discussionmentioning

confidence: 99%

Modelling the vehicle in vehicle—infrastructure dynamic interaction studies

Cantero

O’Brien

González

2009

Proceedings of the Institution of Mechanical Engineers, Part K:

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“…Two other accelerometers were installed in the knuckles at the center of the wheel, one in the front axle and the other in the rear axle, for the accelerations imposed on the unsprung masses of each suspension. Several related works have made or suggested the use of this type of experimental configuration, such as [18,19]. Figure 5 shows the location of accelerometers fitted on the vehicle wheels and the CG.…”

Section: B -Experimental Setupmentioning

confidence: 99%

Comparison between vertical acceleration data from acquired signals and multibody model for an off-road vehicle

Fontoura

Passos²,

Bareta³

et al. 2018

Sci. cum Ind.

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SAE Mini Baja competitions require efforts in developing a reliable vehicle project that enables their teams to manage time and resources wisely. Vehicle simulations are one the best ways to deal with these conditions and prevent failure during a test. This work outlines the methodology that was carried out for validating the multibody dynamics model of a Mini Baja vehicle through vertical acceleration data acquisition. The data was acquired with the vehicle in different sets of obstacles, based on those seen in previously held competitions. Simulation was done through ADAMS/Car, with the vehicle's multibody model being simulated in different three-dimensional roads, counterpart to those where data acquisition took place. Simulation data, when compared to acquired acceleration signals for most of the obstacles, exhibited equivalence. Additional data computation revealed that the spectra in the frequency domain presented most severe loads concentrated between 0 and 20 Hz, incoming mostly from road unevenness. Gathering such data, by the presented approach can assist future analyses and guide the Baja Team in defining an improved project by predicting its dynamic behavior.

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Road profile input estimation in vehicle dynamics simulation

Cited by 97 publications

References 4 publications

Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique

Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique

Modelling the vehicle in vehicle—infrastructure dynamic interaction studies

Comparison between vertical acceleration data from acquired signals and multibody model for an off-road vehicle

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