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The Evolution of Steering Wheel Design in Motorsport
Published in Paul M. Salmon, Scott McLean, Clare Dallat, Neil Mansfield, Colin Solomon, Adam Hulme, Human Factors and Ergonomics in Sport, 2020
James W. H. Brown, Neville A. Stanton, Kirsten M. A. Revell
The requirement for performance optimisation of many of the systems featured on modern vehicles presents drivers with a considerable secondary task. Aspects such as brake bias and differential settings provide a means to optimise the vehicle’s handling characteristics. It can be beneficial to adjust these sometimes on a per-corner basis. In addition to these controls, there exist additional controls for adjusting aspects such as fuelling, energy recovery, and settings profiles that are selected based upon the current tyre compound. A substantial load is placed on the driver when attempting to maintain awareness of the car’s current status and to adjust settings as necessary. Drivers will usually adjust chassis balance-related settings based on their own considerations and feeling of the car; however, team engineers will also speak to the drivers via radio to provide them with requests for settings based on strategy or feedback from in-car systems that they monitor via telemetry. The two-way verbal communication with engineers by radio represents an additional load on the driver. Despite the teams having an awareness of the distractive nature of talking to drivers, they can still occasionally be heard on broadcasts being told to stop talking by the driver who is trying to concentrate. In 2016, the FIA introduced article 20.1 into the regulations stating that, ‘The driver must drive the car alone and unaided’ (FIA, 2017). Whilst this reduced a significant amount of the distraction associated with radio communications, it also meant that the driver was required to be fully aware of the multitude of functions built into their interface. In the event of the driver forgetting a function or needing assistance to select a correct setting, as long as there was no safety implication, the team engineers were not allowed to provide any help. This rule was widely criticised by drivers (PlanetF1, 2016), as the wheels had been designed to a level of complexity with the understanding that engineers could provide guidance as necessary. This additional driver requirement was ultimately considered to be too demanding, and the rule was revoked.
An optimal control approach to the computation of g-g diagrams
Published in Vehicle System Dynamics, 2023
Matteo Massaro, Stefano Lovato, Matteo Veneri
In summary, the QSS vehicle model consists of six equations (namely the three equations of motion (11)–(13), the brake bias constraint (15), the two constraints in (16)) and as many variables. Therefore, once the vehicle speed u and accelerations and are assigned, the QSS equations can be solved to give the unknowns , which define the selected QSS condition. Indeed, the yaw rate is given by and thus is not in the list of unknowns. Alternatively, the lateral velocity unknown can be replaced by the slip angle of the vehicle β, since . This is the preferred choice in this work. The QSS unknowns become
A comparison of free trajectory quasi-steady-state and transient vehicle models in minimum time manoeuvres
Published in Vehicle System Dynamics, 2022
K. Tucker, R. Gover, R. N. Jazar, H. Marzbani
The longitudinal torque controller T accounts for both braking torque component and engine torque component . The controller is restricted so that engine and braking torques are not present together. Two hyperbolic tangent functions are used to acheive this by approximating the Heaviside step function. The constant ϵ, usually greater than 100, is used to balance both the challenges of non-smooth numerical optimisation and the discontinuous nature of the controller. The braking torque calculation is: where maximum braking torques are used to evaluate the brake bias: Maximum braking torques can be determined via braking system modelling or empirical evaluation.
Minimum-lap-time optimisation and simulation
Published in Vehicle System Dynamics, 2021
MLTS for motorcycles are considered for the first time in [16]. The vehicle model includes a roll freedom, in addition to longitudinal, lateral and yaw DOF typical of planar car models. Additional states include the front tyre vertical load, the rear tyre vertical load and the rear tyre lateral force. There are three control inputs: the lateral force on the front tyre, the longitudinal tyre forces and the brake bias that divides the longitudinal force between the front and rear tyres under braking. The physical controls are integrals of the OCP inputs, which are constrained in order to avoid abrupt bang-bang physical inputs. The equations of motion are supplied as a set of implicit differential equations (IDE), which makes the application ‘nonstandard’, since most OCP solvers only deal with ordinary differential equations (ODE). Referring to Figure 4, curvilinear coordinates are employed to track the vehicle's position using the travelled distance along the centreline, the lateral position of the vehicle with respect to the centreline, and the relative orientation α of the vehicle with respect to the tangent to the centreline.