Vehicle Dynamics

Schramm, Dieter; Hiller, Manfred; Bardini, Roberto

doi:10.1007/978-3-662-54483-9

Cited by 36 publications

(24 citation statements)

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“…Therefore, a circle can be drawn representing the maximum acceleration (see Fig. 2), the so-called Kamm's Circle [22]. The longitudinal acceleration in positive xdirection could also be limited further by the drivetrain's power [23].…”

Section: Nds Captured Data and Data Adequacymentioning

confidence: 99%

“…Further discussion regarding the results uses the wellknown single-track model (bicycle model), which is widely used to describe any passenger car's lateral dynamics [22]. It uses the physics of the vehicle to describe the behavior.…”

Section: Vehicle Dynamics Models Using Nds Datamentioning

confidence: 99%

“…For these accelerations, the tire forces are linear and can be represented based on the linear combination of their cornering stiffness and the side-slip angle ( = ). The vehicle state is therefore in the range of validity that holds for this model [22]. Vicente, James, and Anderson [11] state that, especially with limited a priori knowledge, a model with high complexity can suffer from poor model quality.…”

Section: Vehicle Dynamics Models Using Nds Datamentioning

confidence: 99%

See 2 more Smart Citations

Use of Naturalistic Driving Studies for Identification of Vehicle Dynamics

Reicherts

Hesse

Schramm

2021

IEEE Open J. Intell. Transp. Syst.

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This paper discusses the feasibility of data captured in a long-term Naturalistic Driving Study (NDS) for identification of vehicle dynamics. Driving data were captured for over a year. In this data capture, there was minimal effort to define or control everyday driving practices. While the use of real-world data for model parameter identification is a well-known method, NDS are commonly used to explore the behavior of drivers or to analyze real-world traffic situations. Data from NDS have not yet been used for the purpose of parameterizing vehicle dynamics models since everyday drives commonly do not reflect the full range of vehicle dynamics. This leads to the question if the data from an NDS contains the needed information to describe vehicle dynamics accurately. This paper shows that data captured from long-term everyday vehicle usage is sufficient to characterize vehicle dynamics models. It uses lateral vehicle dynamics as an example to show how the data quantity changes the model accuracy and robustness. There is a point where any further data capture produces redundancy and does not add to the overall information. The well-known single-track model serves as the modelling example which offers options to simply compare the derived model behavior with a reference.

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Section: Nds Captured Data and Data Adequacymentioning

confidence: 99%

Section: Vehicle Dynamics Models Using Nds Datamentioning

confidence: 99%

Section: Vehicle Dynamics Models Using Nds Datamentioning

confidence: 99%

See 1 more Smart Citation

Use of Naturalistic Driving Studies for Identification of Vehicle Dynamics

Reicherts

Hesse

Schramm

2021

IEEE Open J. Intell. Transp. Syst.

Self Cite

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show abstract

“…The singletrack model is depicted in Fig.1. The model was derived from 16-state twin-track model described in [1]. The states and parameters of this model are a subset of states and parameters of the full twin-track model.…”

Section: Singletrack Modelmentioning

confidence: 99%

Data-driven identification of vehicle dynamics using Koopman operator

Cibulka

Haniš

Hromčík

2019

2019 22nd International Conference on Process Control (PC19)

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This paper presents the results of identification of vehicle dynamics using the Koopman operator. The basic idea is to transform the state space of a nonlinear system (a car in our case) to a higher-dimensional space, using so-called basis functions, where the system dynamics is linear. The selection of basis functions is crucial and there is no general approach on how to select them, this paper gives some discussion on this topic. Two distinct approaches for selecting the basis functions are presented. The first approach, based on Extended Dynamic Mode Decomposition, relies heavily on expert basis selection and is completely data-driven. The second approach utilizes the knowledge of the nonlinear dynamics, which is used to construct eigenfunctions of the Koopman operator which are known by definition to evolve linearly along the nonlinear system trajectory. The eigenfunctions are then used as basis functions for prediction. Each approach is presented with a numerical example and discussion on the feasibility of the approach for a nonlinear vehicle system.

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“…ρ is the air density, c w is the aerodynamic drag coefficient that describes the shape of the vehicle, A denotes the effective vehicle cross-sectional area, and v vhcl denotes the vehicle velocity ( [24], pp. 212-214).…”

Section: Driving Resistancesmentioning

confidence: 99%

Automated Longitudinal Control Based on Nonlinear Recursive B-Spline Approximation for Battery Electric Vehicles

Jauch

Bleimund

Frey

et al. 2019

WEVJ

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This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4% energy compared to MLC at same average velocity, and achieves a 2.6% higher average velocity than MLC at the same energy consumption.

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Vehicle Dynamics

Cited by 36 publications

References 0 publications

Use of Naturalistic Driving Studies for Identification of Vehicle Dynamics

Use of Naturalistic Driving Studies for Identification of Vehicle Dynamics

Data-driven identification of vehicle dynamics using Koopman operator

Automated Longitudinal Control Based on Nonlinear Recursive B-Spline Approximation for Battery Electric Vehicles

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