This paper examines the problem of minimising stopping distance on split friction roads by joint control of individual wheel brakes and automated steering. A static optimisation problem is introduced that maximises braking on split friction roads without veering out-of-lane. The analysis of the optimal brake forces and counter-steering shows different properties of the optimal solution depending on the degree of split friction assymmetry between the left and right vehicle sides. The solutions are categorised into two regions: small and large split friction asymmetries. At a small split friction asymmetry, all the tyres are at their force peak in their force-slip curves and the slips are small. Therefore, the traditional ABS and a path-following controller give optimal braking and path deviation performance. Whereas at a large split friction asymmetry, it is shown that having all tyres at their force-slip peak is impossible. Instead, allowing larger slips at the low-friction tyres gives maximum braking. Surprisingly, constraining slip constraint on one low-friction tyre limits the available tyre force on the high-friction ones, ultimately leading to longer stopping distances. For braking at large splits, we propose using optimal static solutions to set a feedforward steering angle based on the split friction condition, together with a high-friction brake control to the yaw torque. This control framework is tested in vehicle simulation in the CarMaker environment. Closed-loop simulations of the proposed control framework are compared to an emulated driver response derived from measurements of a professional driver. Stopping distance gains of 6–13% are observed by automating the steering compensation. Further simulations demonstrate that the proposed control framework provides maximum braking even when one side has zero friction.

In high-speed railway systems, the contact force is a crucial metric for describing the interaction performance between the pantograph and the catenary. However, measuring the contact force directly is challenging compared to other measurable quantities such as panhead acceleration and uplift. Leveraging advancements in artificial intelligence, this work proposes a deep learning framework to predict the contact force time history based on panhead acceleration and uplift data. Canonical recurrent neural network models, including vanilla RNN, LSTM and GRU are employed to model and predict the contact force. To enhance efficiency by reducing model parameters and extracting sequential feature maps, we propose a hybrid GRU model integrated with a one-dimensional convolutional neural network, called CNN-GRU. The database for this study is built using a validated and reliable finite element model of the pantograph-catenary system, incorporating variations in structural parameters, speed levels and cut-off frequencies. Four training strategies are designed to evaluate prediction performance across these variations. The results demonstrate the potential of using deep learning to predict contact force based on panhead acceleration/uplift, with some limitations in predicting across different cut-off frequencies. While vanilla RNN, LSTM and GRU models achieve acceptable prediction accuracy, the proposed CNN-GRU outperforms them, particularly in terms of prediction consistency across varying train speeds.

It is a commonly held view that semi-actively (S/A) controlled dampers can significantly improve the behaviour of a road and railway vehicles. Although simulation results published to date indicate potential improvements in railway vehicle ride comfort, this has not been sufficiently verified in real operation. This paper therefore investigates the effectiveness of S/A suspension control using magnetorheological (MR) dampers in improving the ride comfort of railway vehicle in real-world conditions. Field tests were carried out on a electric multiple unit , in which the conventional (passive) vertical and lateral dampers in the secondary suspension were replaced by MR dampers. These experiments were realized on three different railway lines that varied in terms of track alignment, maximum speed and quality. The results demonstrate that S/A control significantly improves ride comfort, especially at higher speeds and on poor-quality tracks. On standard lines, the mean comfort index N MV decreased by an average of 25%. The greatest benefit was achieved at the natural frequency of the vehicle body motion, where the vibration level is reduced by an average of 35%. Despite some challenges, this paper highlights the potential of S/A damping control to improve passenger comfort and makes a recommendations to further optimise its performance.

During the operation of high-speed trains on specific track sections in the late stage of wheel wear, carbody shaking occurs, leading to severe exceedances of dynamic performance limits. This phenomenon is attributed to structural resonance caused by the proximity of the bogie hunting motion frequency to the carbody modal frequency. However, the specific frequency separation margin between the two has not been thoroughly investigated. This study investigates the shaking behaviour, vibration transmission and carbody load characteristics through a comprehensive experiment. Based on non-stationary structural vibration theory, a calculation method for the vibration response under multi-loads is derived, enabling the simulation and reproduction of dynamic failure behaviour. By incorporating in-service load test data, the effects of different load contribution levels and frequency separation margins on shaking vibration suppression are investigated. Ultimately, a design criterion for frequency separation between the carbody modal frequency and bogie hunting motion is proposed. The research results demonstrate that the yaw damper contributes most significantly to shaking mitigation. Ensuring that the first-order diamond modal frequency of the carbody avoids the bogie hunting frequency by 20% effectively reduces the influence of carbody modal vibration, thereby preventing or alleviating the occurrence of shaking problems.

This paper proposes a novel scheme for the provision of steering and guidance for independently rotating wheels (IRWs) in railway vehicles via the use of torsional damping and provides an engineering insight of the possibilities for passive solutions that do not require expensive active control systems. The development of the proposed scheme is presented in detail and computer simulations of a two-axle rail vehicle are carried out to assess the performance. The study demonstrates that the independently rotating wheelset may be effectively controlled via torsional damping between the two wheels of a wheelset to provide the necessary steering on curves and self-centring ability on straight track with irregularities.

Increasing axle loads and speeds in heavy-haul railway systems have intensified rail and wheel damage, leading to elevated maintenance costs and reduced operational efficiency. A promising solution to this issue without compromising service demands is enhancing wheel – rail interaction through optimisation of rail profiles. This study introduces a rail profile optimisation framework tailored for a broad-gauge heavy-haul network experiencing excessive rail wear, utilising Non-dominated Sorting Genetic Algorithm II (NSGA-II). The framework is designed to minimise wear and rolling contact fatigue (RCF) while maintaining satisfactory and safe vehicle dynamic performance. The framework includes optimisation of both high and low rail profiles for sharp and mild curves, as well as optimisation of two rail profiles for tangent track to improve contact point distribution and reduce hollow wear. The optimisation process is based on in-service profiles to ensure practical grindability and incorporates multi-body simulations (MBS) to assess wheel and rail damage as well as vehicle dynamic behaviour. The results indicate that the optimised profiles substantially reduce wear and RCF across various track sections. Furthermore, long-term wear and RCF evaluation of rail profiles on sharp and mild curves confirm the superior performance of optimised profiles, thereby validating their potential for integration into maintenance practices.

The lane-keeping assistance system (LKAS) is key for safety among advanced driver assistance systems (ADAS). It prevents unintentional lane departures and is the standard in most new vehicles. Importantly, its commercial value necessitates minimising false positives resulting from unnecessary early interventions and complying with LKAS regulatory requirements. Therefore, this study proposes a controller that combines model predictive control (MPC) with a control barrier function (CBF), to strictly satisfy regulation-related constraints, even under delayed intervention. The controller was designed to intervene as late as possible for improved commercial value. However, such delayed interventions can reduce the size of feasible sets, interfering with feasibility. Thus, to ensure feasibility, the extended class K ∞ function of the CBF constraint was optimised. The proposed controller was evaluated based on the European New Car Assessment Programme (Euro NCAP) for lane support systems. Experiments were conducted using MATLAB/Simulink and CarMaker, delaying controller intervention to evaluate the commercial value of the controller. To further validate the robustness of the controller, additional experiments were conducted under disturbances, such as crosswinds and additional loads. The results demonstrate that the proposed controller outperforms comparative controllers in terms of satisfying regulation-related constraints under various driving conditions.

Based on the measured data analysis and dynamic calculation results of train wheel wear, this paper reveals the polygonal wear phenomenon of wheels under the influence of eccentricity and its influencing factors. Firstly, the influence of different third media between wheels and rails on the creep characteristics of wheels and rails was studied through the wheel-rail adhesion test bench. On this basis, a ten-degree-of-freedom wheelset vibration system model considering eccentricity was established, and the vibration characteristics of the wheelset and the phenomenon of wheel polygon alignment under different parameters were explored. The research finds that the vibration characteristics of the wheelset are closely related to the wear shape of the wheels. The low-frequency vibration frequency is consistent with the rotation frequency of the wheelset, while the high-frequency vibration frequency is always 355.9Hz and is only excited when the initial creep rate is relatively large. The wear shape is an eccentric multi-order polygon, and the wear phase difference is consistent with the eccentric phase difference between the left and right wheels.

The dynamic simulation technology for the pantograph-catenary system at 350 km/h is well-established. As operational speeds rise to 400 km/h, the influence of wind on the pantograph-catenary system intensifies, and the frequency spectrum of the contact force becomes more complex. This necessitates the optimisation of current simulation methods to accommodate higher operational speeds. To enhance simulation accuracy at 400 km/h, the pantograph is modelled as a fully flexible entity. A neural network approach is utilised to compute the aerodynamic forces acting on the pantograph, whereas a wind speed dataset is leveraged for calculating the aerodynamic forces on the catenary system. Additionally, a spatial contact model is developed to capture the interaction between the pantograph and the catenary with greater accuracy. Consequently, a 3D dynamic numerical simulation method for the pantograph-catenary system at speeds of 400 km/h is introduced. The simulation results demonstrate that this method effectively captures the intricate frequency components of the contact force. A comparison with experimental data reveals a significant reduction in simulation error, from 5.55% using traditional methods to 1.57%.

Articulated passenger vehicles (APVs) represent a transformative approach to urban public transportation, offering a flexible and scalable solution to meet the increasing demand for high-capacity transit systems. APVs combine the advantages of high-capacity rail-based systems with the cost-effectiveness and flexibility of road-based counterparts, making them ideal for urban environments where conventional transport options may fall short. These vehicles, characterised by multiple articulated joints and segmented vehicle units, face particular challenges in dynamic modelling, stability control, and performance evaluation. Active safety and autonomous functions, such as steering assistance, collision avoidance, and automated driving, are critical to improving safety and efficiency in these systems. This paper reviews the state-of-the-art in modelling, control, and performance assessment of APVs, focussing on key aspects such as stability, path tracking, manoeuvrability, and automation. First-principles and data-driven modelling approaches are explored to address the complex dynamics of APVs. Additionally, control strategies for stability and path tracking are discussed, highlighting the importance of integrating active safety and autonomous functions to prevent accidents and improve operational safety and efficiency. The paper aims to provide a holistic perspective on the challenges and opportunities in the development of APVs, contributing to the advancement of smart and sustainable urban transportation systems.