Novel high-fidelity tyre model for motorcycles to be characterised by quasi-static manoeuvres – rationale and numerical validation

Bartolozzi, Mirco; Savino, Giovanni; Pierini, Marco

doi:10.1080/00423114.2021.2013506

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…The authors assumed as valid the empirical flux variation law, presented in [46] and reported in Figure 15, derived from previous experimentation. To assess the actual influence of this parameter, it is necessary to make a complete vehicle model, evaluating suspension displacement and tyre reaction model, developed both for automotive and motorcycle applications (see [50], [51]). In this case, air gap, shown in Figure 16 has been considered constant during the test, assuming a smooth driving (constant speed) with no emergency brakes.…”

Section: Physical Modelmentioning

confidence: 99%

Simulated and real world tests to compare drivers performance in dynamic wireless technology perspective

Berzi

Barbieri

Uggiosi

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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The ongoing electrification of road vehicles needs to be supported by proper growth of charging infrastructure. In this context, dynamic wireless charging can provide a number of advantages, main being the possibility to extend vehicle range without increasing on board battery capacity, potentially reducing cost, mass, and tank-to-wheel energy consumption. The development of such solutions however poses various questions, including the acceptance and the capability of users in driving according to infrastructure characteristics; in particular, the misalignment reduction while a driver follows a straight path positively influence the charging efficiency in dynamic wireless technology. In this paper, authors describe a tests campaign to determine driving performances using both a simulator and a real world equipped vehicle. The research question of this paper is to assess and quantify differences between the two approaches. To reach this objective, in a first phase, data have been collected through a driving simulator (i.e. a full car body mounted on a parallel linked kinematics with a large screen, proposing a virtual city scenario), and in a second phase through a car equipped with a camera. As a post processing phase, statistical tools have been used to describe driving performance indexes and related impacts on wireless charging infrastructure by determining the secondary voltage on the vehicle. Data coming from the activity will be functional to be used by scenario analyst to develop characterization tests only with a simulative approach to decrease costs.

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Section: Physical Modelmentioning

confidence: 99%

Simulated and real world tests to compare drivers performance in dynamic wireless technology perspective

Berzi

Barbieri

Uggiosi

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Tyre forces are a linear combination of tyre slip and camber, including a relaxation behaviour; tyre moments due to turnslip and pneumatic trail are neglected. Tyres are lenticular, leading to lower roll angle values than toroidal wheels [18] unless an overturning moment is included separately [19]. This aspect influences the simulation because the lower roll angle causes lower camber angles and, thus, lower camber thrust, so the slip angles have to change to maintain equilibrium.…”

Section: High-fidelity and Simplified Motorcycle Modelsmentioning

confidence: 99%

“…For the single-track model, the yaw rate ψ is defined as the angular speed around the axis perpendicular to the ground; this is not the case for BikeSim © or most experimental data, where the yaw rate measured by the Inertial Measurement Unit is expressed in the tilting motorcycle frame. In such case, the measured yaw rate measured must be converted into the one relative to the vertical axis [19]:…”

Section: Transient Conditionsmentioning

confidence: 99%

Similarities in steering control between cars and motorcycles: application to a low-complexity riding simulator

et al. 2022

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Motorcycle simulators are employed for rider training, studying human–machine interaction, and developing assistance systems. However, existing simulators are either too simple and, therefore, unsuitable or significantly complex, with higher hardware costs and familiarisation times. This study aimed to use a tuned single-track car model as the basis of a motorcycle simulator, leading to considerable software simplification while preserving its fidelity. In particular, the approach defined a conversion between motorcycle steering torque and car steering angle. It modified the parameters of the latter to reproduce the response of various motorcycle models in quasi-static and transient conditions for different speeds and radii of curvature. A robust manoeuvrability index was chosen. For the car, it was possible to calculate it from its parameters analytically. Next, the car yaw inertia was tuned to obtain a motorcycle-like steering response. Finally, the calibrated car model was implemented into a low-complexity motorcycle simulator for objective validation. It was verified that an understeering single-track model with high yaw inertia has amplitude and phase responses analogous to a motorcycle. The experimental results of the simulator test confirmed these findings for a diverse set of manoeuvres, validating the method. This straightforward approach allows the development of low-complexity simulators with good steering fidelity, using an objective procedure to reproduce the behaviour of a chosen motorcycle class. In addition, the low computational cost of the model makes it a potential candidate for use in assistance systems.

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“…The longitudinal and lateral tire forces of the i-th (i = 1, 2, 3, 4) driven wheel are characterized using the empirical model [34], [35] as:…”

Section: A Vehicle Planar Dynamicsmentioning

confidence: 99%

An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

Chen

Jiang

et al. 2020

IEEE Access

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Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.

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Novel high-fidelity tyre model for motorcycles to be characterised by quasi-static manoeuvres – rationale and numerical validation

Cited by 10 publications

References 25 publications

Simulated and real world tests to compare drivers performance in dynamic wireless technology perspective

Simulated and real world tests to compare drivers performance in dynamic wireless technology perspective

Similarities in steering control between cars and motorcycles: application to a low-complexity riding simulator

An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

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