Analysis of Tire Temperature Influence on Vehicle Dynamic Behaviour Using a 15 DOF Lumped-Parameter Full-Car Model

Perrelli, Michele; Farroni, Flavio; Timpone, Francesco; Mundo, Domenico

doi:10.1007/978-3-030-48989-2_29

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Indeed, through an easy to use of its graphical interface, it is possible to develop controllers according to the well-known Modelbased Control Design approach. The vehicle dynamics model, implemented in the MATLAB/Simulink environment and employed for the evaluation of the control logic performance in vehicle-following maneuvers, is an efficient 15 degrees of freedom lumped-parameter full vehicle model (LPFVM), described in [60] with a MF-based tire model [54]. The LPFVM is based on a set of Ordinary Differential Equations (ODEs) governing the dynamic equilibrium of the vehicle chassis, including three translational and three rotational equilibrium conditions, and of each wheel, comprising a translational equilibrium along the vertical direction and a rotational equilibrium around the spindle axis.…”

Section: Co-simulation Platformmentioning

confidence: 99%

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

et al. 2021

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In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).

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Section: Co-simulation Platformmentioning

confidence: 99%

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

et al. 2021

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“…At each simulation step, vertical forces are estimated by a simple springdamper model. Longitudinal and lateral forces are computed using a simplified version of Pacejka Magic Formula, as explained in detail in [41].…”

Section: Tyre Sub-modulementioning

confidence: 99%

“…The driver sub-module of the simulation software is used to control the throttle position, the steering wheel angle, and the Mz correction factor using three nested PID regulators as described in [40,41] and depicted in Figure 2. A kinematic model is implemented to derive the steering angle at wheels, based on the actual value of the steering input given by the virtual driver and on the steering ratio.…”

Section: Virtual Drivermentioning

confidence: 99%

“…Moreover, a vDiL agent module was programmed. In particular, our testing scenarios exemplify how the proposed simulation platform enabled the comparison of the lateral dynamics of two vehicles with identical physical characteristics, but with two different strategies for driving torque distribution, i.e., a simplified mechanical differential model [40,41] and a DYC-based torque vectoring (TV) system [42].…”

Section: Introductionmentioning

confidence: 99%

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On the Benefits of Using Object-Oriented Programming for the Objective Evaluation of Vehicle Dynamic Performance in Concurrent Simulations

et al. 2021

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Assessing passenger cars’ dynamic performance is a critical aspect for car industries, due to its impact on the overall vehicle safety evaluation and the subjective nature of the involved handling and comfort metrics. Accordingly, ISO standards, such as ISO 4138 and ISO 3888, define several specific driving tests to assess vehicle dynamics performance objectively. Consequently, proper evaluation of the dynamic behaviour requires measuring several physical quantities, including accelerations, speed, and linear and angular displacements obtained after instrumenting a vehicle with multiple sensors. This experimental activity is highly demanding in terms of hardware costs, and it is also significantly time-consuming. Several approaches can be considered for reducing vehicle development time. In particular, simulation software can be exploited to predict the approximate behaviour of a vehicle using virtual scenarios. Moreover, motion platforms and detail-scalable numerical vehicle models are widely implemented for the purpose. This paper focuses on a customized simulation environment developed in C++, which exploits the advantages of object-oriented programming. The presented framework strives to perform concurrent simulations of vehicles with different characteristics such as mass, tyres, engine, suspension, and transmission systems. Within the proposed simulation framework, we adopted a hierarchical and modular representation. Vehicles are modelled by a 14 degree-of-freedom (DOF) full-vehicle model, capable of capturing the dynamics and complemented by a set of scalable-detail models for the remaining sub-systems such as tyre, engine, and steering system. Furthermore, this paper proposes the usage of autonomous virtual drivers for a more objective evaluation of vehicle dynamic performances. Moreover, to further evaluate our simulator architecture’s efficiency and assess the achieved level of concurrency, we designed a benchmark able to analyse the scaling of the performances with respect to the number of different vehicles during the same simulation. Finally, the paper reports the proposed simulation environment’s scalability resulting from a set of different and varying driving scenarios.

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“…Generally, they can be divided into two categories, i.e., kinematic and dynamic model [6]. Both contain a vast set of models, varying from 1-Degree-of-Freedom (DoF) integrator [7] to 15-DoF full car [8]. This paper focuses on the longitudinal and lateral control of vehicle, which involves 3 DoFs in the 𝑥 − 𝑦 planar coordinates.…”

Section: Imentioning

confidence: 99%

Numerically Stable Dynamic Bicycle Model for Discrete-time Control

Sun

et al. 2021

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)

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Dynamic/kinematic model is of great significance in decision and control of intelligent vehicles. However, due to the singularity of dynamic models at low speed, kinematic models have been the only choice under many driving scenarios. This paper presents a discrete dynamic bicycle model feasible at any low speed utilizing the concept of backward Euler method. We further give a sufficient condition, based on which the numerical stability is proved. Simulation verifies that (1) the proposed model is numerically stable while the forward-Euler discretized dynamic model diverges; (2) the model reduces forecast error by up to 49% compared to the kinematic model. As far as we know, it is the first time that a dynamic bicycle model is qualified for urban driving scenarios involving stopand-go tasks.

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Analysis of Tire Temperature Influence on Vehicle Dynamic Behaviour Using a 15 DOF Lumped-Parameter Full-Car Model

Cited by 6 publications

References 10 publications

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers

On the Benefits of Using Object-Oriented Programming for the Objective Evaluation of Vehicle Dynamic Performance in Concurrent Simulations

Numerically Stable Dynamic Bicycle Model for Discrete-time Control

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