Search Results (185)

Reducing traction energy in urban rail systems while preserving safety, punctuality, and passenger comfort remains challenging. Additionally, route-level tram studies that train deep reinforcement learning (DRL) policies using Operational Train Monitoring Recorder (OTMR) logs and benchmark them across multiple objectives remain limited. This study develops and evaluates a Human-Assisted Deep Reinforcement Learning (HADRL) framework for multi-objective tram control in an OTMR-grounded simulation. Two HADRL agents were trained using a human-assistance action mapping: a standard Proximal Policy Optimisation (PPO) baseline and a recurrent, history-augmented PPO. Their performance was compared against that of four human drivers using indices for speed-limit compliance, schedule deviation, traction energy, jerk-based comfort, and stopping accuracy. These performance measures were aggregated using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with both equal and entropy-derived weights. Both HADRL agents reproduce the characteristic accelerate–coast–brake driving pattern, reduce traction energy relative to all human baselines, and achieve near-complete speed-limit compliance, all while remaining within the specified schedule-deviation and comfort thresholds. TOPSIS yields identical rankings under both weighting schemes, with Multi-Objective Tram Operation Non-Stationary Proximal Policy Optimisation (MOTO-NSPPO, a recurrent, history-augmented PPO) ranked first and PPO second. Full article

The growing importance of integrating renewable energy sources (RESs) into mainline railway traction networks stems from the sector’s substantial electricity demand, which is traditionally met by carbon-intensive thermal generation. This paper addresses the potential of wind power to enhance energy efficiency and reduce emissions in rail transport. It details the development of digital models for simulating DC traction power systems (TPSs) coupled with RESs, specifically wind turbines. Given the complexity of TPSs, effective integration requires digital modeling that accounts for their unique properties. The proposed methodology, based on phase coordinate algorithms, offers a universal and comprehensive framework. It enables the identification of various operational modes (normal, emergency, and special) for diverse network components, including traction networks, transmission lines, and transformers. These models were used to simulate real-world train operations, generating data on electrical parameter dynamics and transformer thermal conditions. The results confirm that wind integration can improve energy efficiency, validating the methodology’s practical applicability for RES projects in DC traction networks, including advanced high-voltage systems. Full article

As well as the off-line phenomenon between the pantograph strip and the contact wire that occurs frequently, the current collection quality of trains is potential under threat. Pantograph arcing can bring about overvoltage and harmonics in the traction circuit, which can seriously threaten the construction’s strength and efficiency of current collection. Meanwhile, the electrified railway might meet very complex environments, including the various routes under different air pressures. When the train runs in a medium- or low-pressure area, the reduction in air pressure may result in significant differences in the dynamic evolution characteristics of pantograph arcing. So it is necessary to carry out a detailed study on the influence of pantograph arcing on the current collection of electrified trains in a low-pressure environment. In this paper, we proposed an improved pantograph arcing model suitable for medium-to-low-pressure regions, with the pressure parameters taken into consideration. Furthermore, we examined the influence of pantograph arcing under medium-to-low-pressure environments on the traction power supply system. The arcing dynamics, including the arc duration, the current zero-crossing, and the arcing-released energy at different air pressures were compared. The overvoltage and the harmonic distribution of the traction drive system were also analyzed. This work may be helpful for the design and maintenance of electrified railways under medium-to-low-pressure environments. Full article

With the expansion of urban rail transit, increased train operation density, and the large-scale grid integration of renewable energy such as offshore photovoltaic power, traction power supply systems face stricter requirements for operational safety, power supply reliability and energy utilization efficiency. Offshore photovoltaic power, integrated into the traction power supply network via flexible DC transmission technology, promotes renewable energy consumption, but its random and volatile output overlaps with time-varying traction loads, increasing the complexity of DC-side fault characteristics and protection control. Flexible DC technology is a core direction for next-generation traction substations, and three-level converters (key energy conversion units) have advantages over traditional two-level topologies. However, their P-O-N three-terminal DC-side topology introduces new faults (e.g., PO/ON bipolar short circuits, O-point-to-ground faults), making traditional protection strategies ineffective. In addition, wide system current fluctuation (0.5–3 kA) and offshore photovoltaic power fluctuation easily cause fixed-threshold protection maloperation, and the coupling mechanism among modulation strategies, DC bus capacitor voltage dynamics and fault current paths is unclear. To solve these bottlenecks, this paper establishes a simulation model of the system based on the PSCAD/EMTDC(A professional simulation software for electromagnetic transient analysis in power systems V4.5.3) platform, analyzes the transient electrical characteristics of three-level converters under traction and braking conditions for typical faults, clarifies the coupling mechanism, proposes a condition-adaptive fault identification strategy, and designs a reconfigurable fault energy handling system with bypass thyristors and adaptive crowbar circuits. Simulation and hardware-in-the-loop (HIL) experiments show that the proposed scheme completes fault identification and protection within 2–3 ms, suppresses fault peak current by more than 70%, limits DC bus overvoltage within ±10% of the rated voltage, and has good post-fault recovery performance. It provides a reliable and engineering-feasible protection solution for related systems and technical references for similar flexible DC system protection design. Full article

Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling. Full article

Improving energy efficiency is critical for the sustainable development of urban public transportation. Regenerative braking is widely employed in urban rail transit to recycle braking kinetic energy into the traction network, thereby enhancing system efficiency. However, without effective scheduling, excessive feedback energy can induce instantaneous voltage spikes, leading to line overheating and accelerated equipment aging. Existing studies often fail to fully address these challenges due to simplified physical models and limited adaptability to real-time environments. To overcome these limitations, this study proposes a dynamic scheduling method for the efficient utilization of regenerative energy within a train fleet. A physical simulation system featuring a “Network-Train-Control” three-layer architecture is constructed. By formally describing the physical coupling among network topology, operational rules, and train kinematics, the system enables accurate energy profiling under realistic impedance and signaling constraints. Furthermore, a finite state automaton (FSA) is utilized to abstract continuous train dynamics into discrete states, facilitating a braking-event-triggered Model Predictive Control (MPC) framework. This framework predicts and dynamically adjusts fleet operations within a receding horizon to maximize the immediate absorption of regenerative energy. Experimental results demonstrate that the proposed method achieves active energy cooperation among trains, increasing the regenerative energy utilization rate by approximately $11\%$, thereby offering a viable technical solution for low-carbon urban transit. Full article

Finite element analysis (FEA) remains the gold standard for simulating piezoelectric microactuators because it resolves coupled electromechanical fields with high fidelity. However, transient FEA becomes prohibitively expensive when thousands of actuators must be simulated. This work presents a data-driven surrogate modeling framework for tileable, PZT-5H microactuators enabling fast, dynamic, and parallel predictions of actuator displacement over multi-step horizons from short displacement history windows, augmented with the corresponding prescribed voltage and traction samples over that same history window. High-fidelity COMSOL simulations are used to generate a dataset aiming to encompass the full operational envelope of our actuator under stochastically sampled and procedurally generated input waveform families. From these families, we construct a supervised learning dataset of time histories, displacement, and applied loads. From this, we train a recurrent sequence-to-sequence neural network that predicts a multi-step open-loop displacement rollout conditioned on the most recent electromechanical history. The resulting model can be leveraged to perform batched inference for millions of actuators on GPU hardware, opening up a wide range of new applications such as reinforcement learning via digital twins, scalable design and simulation for piezoelectric artificial-muscle systems, and accelerated optimization. Full article

Irregular wheel wear can significantly degrade wheel–rail interaction performance and, in severe cases, compromise the safety of high-speed trains. Accurate and timely monitoring of wheel wear is crucial for maintaining operational reliability. Existing monitoring methods often rely on high-end sensors or are sensitive to environmental disturbances, limiting their practical deployment. This study proposes a novel method for monitoring irregular wheel wear by analyzing the stator current spectrum of traction motors. Firstly, an electromechanical coupled model is developed by integrating the electric drive system with the vehicle–track dynamic model to capture the propagation of wear-induced excitation. The effect of polygonal wear on the stator current is investigated, revealing the presence of harmonic components coupled with the wear excitation frequency. To extract these features, a comb filter based on Variational Mode Decomposition (VMD) is introduced. The method effectively isolates wheel wear-related harmonics from existing electrical harmonics in the stator current signal. Simulation results demonstrate that the proposed approach can accurately detect harmonic features caused by polygonal wear, validating its applicability. This method provides a feasible and non-intrusive solution for wheel wear monitoring, offering theoretical support for condition-based maintenance of high-speed rail systems. Full article

Traditional diode-based rectifiers (TDRs) in railway traction substations (TSSs) are inefficient at handling bidirectional power flow and cannot recover regenerative braking energy (RBE). Replacing these conventional systems with reversible traction substations (RTSSs) requires detailed modeling, extensive simulations, and validation using real data. This paper presents a DT-oriented real-time modeling and Hardware-in-the-Loop (HIL) platform for the analysis and performance assessment of RTSSs in DC railway systems. The integration of interleaved PWM rectifiers enables bidirectional power flow, allowing efficient RBE recovery and its return to the main grid. Modeling railway networks with moving trains is complex due to nonlinear dynamics arising from continuously varying positions, speeds, and accelerations. The proposed approach introduces an innovative multi-train simulation method combined with low-level transient and power-quality analysis. The validated DT model, supported by HIL emulation using OPAL-RT, accurately reproduces real-world system behavior, enabling optimal component sizing and evaluation of key performance indicators such as voltage ripple, total harmonic distortion, passive-component stress, and current imbalance. The results demonstrate improved energy efficiency, enhanced system design, and reduced operational costs. Meanwhile, experimental validation on a small-scale RTSS prototype, based on data from the Italian 3 kV DC railway system, confirms the accuracy and applicability of the proposed DT-oriented framework. Full article

Background: Medical mediation offers a patient-centered, collaborative alternative to traditional resolution methods for healthcare conflicts that is gaining international traction in an increasingly complex environment of advancing technology and diverse patient populations. This systematic review aims to synthesize the literature on medical mediation and analyze its clinical applications, conflict typologies, involved actors, mediation methodologies, legal frameworks, and theoretical underpinnings. Methods: A systematic search was conducted in PubMed and Scopus for English-language articles published between 1984 and 2025. Results: Of 656 initial records, 152 studies met the inclusion criteria and were categorized across six domains: clinical context, actors involved, conflict type, mediation framework, legal/policy structure, and theoretical foundations. Most studies originated from high-income countries, particularly the U.S. and U.K., with notable expansion after 2010. Medical mediation was most frequently applied in bedside care, end-of-life decision-making, and managed-care disputes. While ethics consultants were the primary mediators, increasing involvement of trained clinicians and institutional actors was also observed. Most studies emphasized generic bioethical mediation frameworks, with some focused on formalized models and training. Legal frameworks varied, and an increasing number of countries have been adopting institutional or national programs to support mediation. Conclusions: Medical mediation is an efficient tool for resolving complex clinical conflicts, enhancing communication, and preserving therapeutic relationships. Its institutionalization, through law and training, is key to the promotion of justice, transparency, and ethical integrity in modern healthcare systems. Full article

This article addresses selected aspects of designing a Traffic Management System (TMS) for the railway component of Poland’s Central Communication Port (CPK) project. The primary objective was to determine train headway times while considering automated traffic conflict resolution and speed profile optimization in relation to traction energy consumption. The study utilized simulations in the MATLAB/Simulink (Version number: R2024a Update 3) environment, modeling the movement of an ETR610 (ED250) train on a line equipped with the European Train Control System (ETCS). The simulation results provided insights into the impact of the adopted assumptions on TMS operational efficiency under failure conditions and its capability to optimize train movements. The conclusions underscore the critical importance of time reserves in effective conflict resolution, the interplay between buffer allocation and speed restrictions, and the impact of minimizing train stops on energy consumption. They also highlight the necessity of adapting operational strategies to infrastructure characteristics and the influence of simulation time on the effectiveness of conflict resolution methods. Furthermore, the study emphasizes the need to broaden operational scenarios to include failures of traction vehicles and train control systems, along with appropriate planning for time reserves. Full article

This paper addresses the issue of abnormal fluctuations in brake pipe pressure causing variations in braking force, or even forced stops, in heavy-haul trains. A multi-parameter synchronous acquisition monitoring device has been designed to collect relevant operational parameters during train movement. Integrating train traction calculation methods, algorithmic reasoning is conducted to assess the impact of abnormal pipe pressure fluctuations on braking force. Utilising the derived computational approach, the effect of such pressure anomalies on train braking force is calculated. Train braking force is regulated through control of the train pipe pressure reduction. Both train pipe pressure and pressure reduction are managed by the locomotive via the equalising air chamber. Traditional detection methods focus on pressure reduction and air charging/discharging times, making it difficult to analyse fluctuation causes in-depth. This study installs pressure sensors on the locomotive brake’s equalising air chamber and the train pipe inspection port to collect pressure data. It simultaneously records parameters such as ambient temperature and atmospheric pressure. Utilising the monitoring data, it calculates the impact of pipe pressure fluctuations on train air braking force, thereby supporting improvements in braking system stability and operational safety. Full article

As the core subsystem of high-speed trains, the reliable operation of the traction system is critical to ensuring train safety. To enhance fault detection performance, this study proposes a probability-related slow feature analysis (PRSFA) method that leverages the intrinsic characteristics of the traction system. Specifically, Kullback–Leibler divergence is incorporated into the conventional slow feature analysis framework. Based on the slow features extracted from traction system data, the probability distribution distance between offline and online features is further computed to construct detection statistics. The feasibility of the proposed approach is validated using the high-speed train traction system simulation platform developed by Central South University. Compared with the existing SFA, DSFA and DWSFA methods, the results show that the PRSFA method can effectively improve the accuracy and robustness of fault detection. Full article

This study presents an Artificial Neural Network (ANN)-based adaptive switching frequency control strategy for series Direct current (DC) motor drives used in battery-powered mining locomotives, aiming to extend the lifetime of critical power-electronic components such as Insulated Gate Bipolar Transistors (IGBTs) and DC bus capacitors. In embedded systems for electric traction, two dominant degradation factors, motor current ripple and IGBT temperature fluctuation, significantly shorten component lifetimes. Conventional fixed switching frequencies impose a trade off: higher frequencies reduce current ripple but increase IGBT losses and temperature, while lower frequencies yield the opposite effect. Consequently, an adaptive variable switching frequency control algorithm is proposed to perform real-time decision making by predicting the optimal switching frequency that minimizes both motor current ripple and IGBT thermal fluctuations. The proposed algorithm was trained with a dataset acquired from current sensors, NTC temperature sensors, and a potentiometer defining the target current (PWM duty). Performance comparisons with a fixed frequency demonstrate that the ANN-driven approach maintains an average current ripple of less than 5% (average) and 10% (maximum), while the lifetime of the IGBT and capacitors improves. A fairness index was defined to quantify the relative lifetime improvement of the IGBT and capacitor, revealing that the proposed variable frequency switching model enhances the overall system performance by up to 13 times compared to fixed-frequency operation. These results confirm that the integration of embedded machine learning and adaptive control algorithms can substantially enhance the durability and efficiency of power-electronic systems in real-time industrial applications. Full article

Ventilation systems in railway tunnels are crucial for ensuring the safe operation of trains, particularly those powered by diesel locomotives. Inadequate ventilation design may cause serious traffic accidents. Previous studies were generally focused on tunnel ventilation issues for highway tunnels or high-speed railway tunnels, while little attention has been paid to systematic ventilation design for diesel locomotive railway tunnels. To summarize the research progress and find a sustainable solution of ventilation for diesel locomotive railway tunnels, a comprehensive review of the relevant literature was conducted in this paper. First, the development history of diesel locomotives is traced, and the main framework and key components of a diesel locomotive railway ventilation system are introduced. Then, the limit values of locomotive emissions within tunnels specified in different standards from different countries are compared. Finally, key factors affecting the performance of ventilation systems in diesel locomotive railway tunnels are sorted. It is found that diesel locomotives remain the primary choice for railway freight traction in developing countries and specific challenging environments, such as high-altitude areas and permafrost regions. In the ventilation design for tunnels in these regions, particular attention must be paid to pollutants like CO, NO, and NO₂. Ventilation efficiency is influenced by numerous factors, including tunnel geometry, internal systems, and train operating conditions. Intelligent ventilation control presents a promising sustainable solution to address future demands. This review can provide a reference for subsequent research on ventilation technologies, low-carbon retrofitting, and sustainable development practices for diesel locomotive railway tunnels. Full article