Introducing Functional Data Analysis to Coast Down Modeling for Rolling Resistance Estimation

Andersen, Lasse Grinderslev; Larsen, Jesper K.

doi:10.4271/2015-01-9111

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…For Equations (1)-( 3), the optimal values for air density, the frontal area of the vehicle, the velocity of the vehicle, the velocity of the wind, the vehicle mass, the acceleration due to gravity, and theta can be easily found because they are measured, calculated, or known constants. The aerodynamic drag and rolling resistance coefficients are more difficult to obtain [15,16]. This research aims to optimise these two coefficients by minimising the error between the real and modelled energy consumption profiles of the data collected during the trip from South Africa to Namibia.…”

Section: Optimisationmentioning

confidence: 99%

“…A standard method for finding these two coefficients is to use coast-down tests [15][16][17]. These tests are challenging to implement as perfect conditions rarely occur.…”

Section: Optimisationmentioning

confidence: 99%

“…These components can be seen in Equations ( 1)-( 3). Each variable is either a constant or can easily be measured or calculated, except for the coefficients of aerodynamic drag and rolling resistance [15,16]. The measured or calculated variables can be seen in Tables 1 and 2.…”

Section: Modellingmentioning

confidence: 99%

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Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles

Van Greunen,

Oosthuizen

2023

WEVJ

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This research investigated an alternate method for establishing the complex coefficients used in an electric vehicle’s mathematical energy consumption model. While other methods for creating electric vehicle energy models exist, it would be beneficial to have a rapid and inexpensive technique that remains accurate. Producing a mathematical energy model for such a vehicle has the challenge of determining its aerodynamic drag and rolling resistance coefficients. Currently and most often, expensive and tedious (time-consuming) methods are used to find these coefficients. Computational fluid dynamics (CFD), wind tunnel testing, and extensive mathematics make this objective challenging. For this work, a solar-powered electric vehicle provided the source data to derive its coefficients cost-effectively and efficiently. Data were collected during a road test of the solar electric vehicle from South Africa to Namibia stretching over 2000 km, in which all required energy variables were recorded. The collected data were used in an optimisation routine to establish the two coefficients by minimising the actual and modelled energy consumption error and controlling the driving speed. The outcome of the optimisation routine produced accurate coefficients with a final error value of less than 5% when applied to a validation data set not used during optimisation. With minor modifications, this method may be integrated into any electric vehicle computer system to autonomously identify its two hard-to-find coefficients while driving, which can be used to provide an accurate and realistic driving range estimation to the driver.

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Section: Optimisationmentioning

confidence: 99%

“…A standard method for finding these two coefficients is to use coast-down tests [15][16][17]. These tests are challenging to implement as perfect conditions rarely occur.…”

Section: Optimisationmentioning

confidence: 99%

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Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles

Van Greunen,

Oosthuizen

2023

WEVJ

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“…Coast-down modelling has been widely used to assess vehicle aerodynamic drag and rolling resistance by fitting a vehicle resistance model to speed measurements and thereby get an estimate on model parameters (Andersen, Larsen, 2015). A coast-down model was used for assessing how road surface characteristics influence rolling resistance.…”

Section: Introductionmentioning

confidence: 99%

Archives of Acoustics

Abouel-Seoud

2019

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The main objective of the research presented in this paper is to enhance driver-passengers comfort of a vehicle that in turn leads to better vehicle safety and stability. The focus was put on studying the interior vibration and noise contributions originated from tire-road and engine-transmission subsystems, due to their significant impact on the dynamic performance of the vehicle. The noise and vibration measurements were recorded at the driver's head position and on the driver legs room. Furthermore, the influence of different tire types and road surface textures on the vehicle interior noise and vibration were considered. The results indicate that the widely used conventional engine mounts and tires in commercial vehicles cannot fulfill the conflicting requirements for the best isolation concerning both road surface and engine-transmission induced excitations. The values of driver's head position sound pressure level and floor vibration acceleration broadband averages originate for engine-transmission are lower than that for tire-road interaction. Furthermore, the values of RMS, crest factor, kurtosis and IRI for the vehicle waveform were estimated for vehicle speeds, tire types and road surface textures. Moreover, the percentage contribution for both interior noise and vibration originated from tire-road interaction is higher than the one from vehicle engine-transmission system in all the vehicle speed range, tire type and road surface texture considered.

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Introducing Functional Data Analysis to Coast Down Modeling for Rolling Resistance Estimation

Cited by 2 publications

References 16 publications

Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles

Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles

Archives of Acoustics

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