Overhead conductor rail (OCR) serves as a crucial device employed in tunnels to power electric locomotives. The OCR is hung by supports mounted on the tunnel ceiling. Previously, the support was usually considered as a linear spring when modelling the OCR. The linear stiffness and mass adopted to the support model were imprecise, and damping was absent. It may be insufficient to reproduce the pantograph-OCR system (POCR) dynamic behaviour. This paper is devoted to improving the accuracy of POCR dynamic model and makes it more realistic. Field tests are conducted in order to identify the support parameters, including stiffness, damping and mass, for the first time. Then, a novel nonlinear stiffness-damping-mass support model is developed utilising the identified parameters and verified by comparing simulation results with measurement data. Finally, in pursuit of improving current collection quality, the effects of support damping and train speeds on contact forces are investigated. The results show that the support damping can mitigate the OCR’s vibration. The increase of support damping within a specific range, 0–200 Ns/m, has a positive effect on the current collection quality.

Adhesion estimation in wheel and rail interface is a crucial element as it has a direct effect on the locomotive characteristics and performance. Since the adhesion condition depends on unpredictable factors and dynamic models, its estimation is a complex task. In this paper, a dynamic model for a single wheelset including parameters in detail is developed to improve the accuracy of the model. Then, a model-based Proportional–Integral Observer (PIO) is designed to estimate adhesion during uncertainties and nonlinear parameters. The stability of the proposed PI observer is proved with Lyapunov theory and Linear Matrix Inequality (LMI). Finally, the performance of the method is evaluated via numerical simulation in MATLAB software, and it is validated with a model set-up in the SIMPACK Multibody Simulation (MBS) software.

Currently, simulation is an important method for studying the interaction between the pantograph and the rigid overhead contact system (ROCS). However, when solving the ROCS model for long railway line scenarios, the problem of intractable or inefficient computation arises due to the excessively large size of the matrix. To address this issue, this paper proposes a method called: a fast solution method for the interaction between the rigid overhead contact system and pantograph on long railway lines based on a coupled window approach (hereinafter referred to as: CW-RCP). The CW-RCP method utilises coupled windows to simulate the ROCS model, effectively limiting the length of the ROCS involved in each computation. This approach greatly enhances computational efficiency. After deriving the dynamic equations and solving equations of the CW-RCP method in detail, the effectiveness, advantages, and applications of this method are discussed. The results demonstrate that the CW-RCP method can effectively compensate for the computational inefficiencies of traditional methods without compromising computational accuracy.

ABSTRACT In rally racing, a high sideslip cornering technique known as drifting is employed to minimise the lap time. However, for autonomous vehicles, tracking the trajectory while drifting is a challenging control problem due to the conflict between the two objectives. This paper proposes a novel model predictive controller (MPC) with two operation modes to address this conflict. In Mode 1, fuzzy expert strategies combined with the lateral error are designed to calculate the drift equilibrium point. Subsequently, the linear MPC controller is utilised to achieve quick adjustments of the lateral error. In Mode 2, terminal state constraints and error constraints are added to the MPC optimisation problem. The stability of the MPC controller is guaranteed to keep the vehicle within the expected error range. Finally, a comparison between the existing expert controller and the proposed controller is carried out on both the CarSim platform and a 1:10 scale race car. The results demonstrate that the proposed controller outperforms the existing expert controller in terms of tracking performance and drift stability.

ABSTRACT Existing mathematical driver models with steering torque feedback are reviewed. It is argued that a new linear parametric driver-steering-vehicle model is required to provide greater insight to subjective assessment of steering torque feedback. The proposed new model incorporates: neuromuscular dynamics; proprioceptive (muscle stretch) and visual sensory dynamics; steering torque perception via efference copy; process and measurement noise; state estimator; and an internal predictive model of the plant. A series of path-following experiments with thirteen test subjects is performed on a fixed-base driving simulator. A Box-Jenkins model accounted for 90% of the variance of the measured handwheel angle time series averaged over the test subjects (VAF). The unknown parameter values of the model were identified from the driving simulator data. The prediction accuracy approached the upper bound given by the Box-Jenkins model. Process noise was found to be linearly dependent on muscle activation torque, and this signal-dependent behaviour was also present in the measurement noise. The model was re-identified using signal to noise ratios as parameters. The VAF was 86.6%, close to that of the Box-Jenkins model, and the identified parameter values are physically plausible, giving confidence that the model is suitable for further work on understanding steering torque feedback.

ABSTRACT To improve the wagon brake performance and to investigate the wheel skidding on the rail during braking, the comprehensive rail freight wagon models with consideration of braking are generated by using the Gensys software package, which includes the masses of a wagon carbody, two bolsters, four sideframes, eight axle boxes and eight wheelsets. The nonlinear characteristics of secondary and the primary suspensions are considered among the mass connections. The tangential wheel-rail contact creepages and creep forces are calculated by a modified FASIM method with consideration of decreasing changes after saturation points. The brake type of air-pressed friction shoe on wheel tread surface is taken into account and the reference brake force profiles are considered with the characteristics of increasing friction coefficient with wagon speed decreasing. The wagon rolling resistance is also considered by using the equations used in Australian rail industries. The wagon braking stopping distance at an operation speed and the wheel slipping and skidding during braking situations can be simulated, and the effect of wheel-rail surface conditions (dry, wet, low coefficient friction (LCF) modifier and grease), wagon loading distribution, etc., to the braking performance can be examined, and the discussions on the wagon braking operation are given.

ABSTRACT A three-dimensional (3D) finite element model of a railway crossing panel for use in multibody simulation (MBS) of dynamic vehicle–track interaction is presented. It is a two-layer track model with stock rails and sleepers represented by beam elements and a crossing rail represented by 3D solid elements. The track model uses linear bushings for the rail fastenings and bi-linear bushings for the ballast to allow for potentially voided sleepers. Based on the output from the MBS, the structural loading of the crossing in terms of strains, stresses and sleeper-ballast contact pressures is extracted in a post-processing step. The model is calibrated and validated to measurement data from a comprehensively instrumented switch & crossing (S&C) demonstrator installed in the Austrian railway network as a part of the European research programme Shift2Rail. The applied procedure for the calibration and critical assessment of the crossing model is described in detail. It is based on a model parameterisation with eight parameters relating to the rail fastening and foundation stiffnesses and to a distribution of the ballast voids. The calibration method uses Latin hypercube samples to explore the parameter space in a sensitivity analysis before a parameter optimisation is performed using a gradient-based method on a response surface built from a polyharmonic spline. In a comparative study it is shown that the 3D model and a more conventional beam model of the crossing rail show similar calibration results and good agreement with the measured data. The 3D model allows for the extraction of stress concentrations in the crossing rail but has an increased computational time of about 30% compared to the beam model.

ABSTRACT In this paper a relatively simple model is presented, able to compute the loads on the suspension arms and at the tyres' contact patches of a Formula SAE car starting from telemetry data. The model is based on standard dynamic equilibrium equations, a simplified assumption for the limited-slip differential and a given front-to-rear brake distribution. The model inputs are the signals acquired with common telemetry sensors, which usually equip a Formula SAE vehicle (Inertia Measurement Unit, GPS, potentiometers, Hall sensors). In order to validate the model, some arms of the suspensions were instrumented with strain gauges, and a kinematic model of the suspension, together with its static equilibrium equation set, was set up to relate the arm loads to the forces acting at the tyre-road contact patch. The suspension kinematic and equilibrium models were first validated through static tests, with known forces applied at the wheel hub. Then, the complete vehicle model was validated by comparing the same quantities, relative to some real driving tests on a kart circuit, showing a fairly good agreement between the predicted and the measured loads. The model represents a valuable tool for vehicle design and development for the Formula SAE team.

This paper presents a finite element model of a railway crossing panel for use in multibody simulations (MBS). It is a two-layer track model with rails and sleepers represented by beam elements that use linear bushings for rail fastenings and non-linear bushings for ballast. The model is calibrated and validated to measurement data from a comprehensively instrumented switch and crossing demonstrator installed in the Austrian railway network as a part of the European research programme Shift2Rail. The validation concerns the capability of the model to capture the structural response of the crossing panel under traffic loading after calibration of physical track parameters to realistic values. The structural response is measured in the form of displacements, strains, and sleeper-ballast contact forces. It is shown that the developed model can represent the measured track responses well and that it was necessary to account for a varying sleeper-ballast gap distribution along the crossing transition sleeper to obtain good agreement. The calibration uses Latin hypercube samples to explore the parameter space in a sensitivity analysis before a parameter optimisation is performed using a gradient-based method on a response surface built from a polyharmonic spline.

A simulation investigation of emergency evasive maneuvers of the type experienced during obstacle avoidance is conducted for long combination vehicles at highway speeds. The dynamic model includes a commercial truck with a configuration that is typical of what is used in the U.S., with two 28-ft trailers connected to the tractor through a fifth wheel, and to each other by an A-dolly. The model is validated against test data and used for evaluating distance and time needed to safely perform a steering maneuver to avoid an obstacle on the road. The simulation results indicate a significant difference in both the required time and distance for safely exercising an emergency obstacle avoidance, ranging from approximately 151–276 ft in distance and 1.7–2.4 s from the start of the driver action. The reaction time of the driver will add to both measures by an amount corresponding to the lapse in time between the moment the driver recognises the obstacle and when a steering action is initiated. To the extent that the lighting and road condition add to the reaction time, a significantly larger distance and longer time are needed during nighttime driving as compared with daytime, under similar road and loading conditions.