As CT Research Articles

Efficient Wheel-Rail Stick-Slip Numerical Modeling for Railway Traction Vehicles

Motor railway vehicles necessitate enhanced control of wheel-rail contact mechanics to ensure optimal adhesion. During train running, driving wheelsets exhibit torsional vibrations that compromise adhesion and potentially lead to axle damage. Consequently, the development of dynamic models for analyzing driving wheelset stick-slip phenomena and control strategies is an area of significant research interest for traction control, studies on rail corrugation, and locomotive drivetrain design. Despite their application in various railway vehicle problems, non-smooth models have not been explored as an alternative for analyzing stick-slip, and existing research has focused on extensive computations based on Kalker’s theory or simplified models using constitutive friction laws. This work demonstrates the efficacy of non-smooth models in studying motor wheelset stick-slip. The non-smooth approach is suited for control systems, prioritizes simplicity while capturing the essential friction characteristics, and enables efficient dynamic simulations. The proposed model incorporates a set-valued friction law, and the equations of motion are formulated as a switch model. Numerical integration is achieved through an event-driven algorithm. The paper showcases application examples for the model. A direct comparison with an equivalent model using a constitutive friction law shows that the non-smooth integration is an order of magnitude more efficient in the stick phase.

A method of protecting an articulated electric bus from side overturning

Justification. The current trend towards the use of electric buses has been growing in the last few years. Articulated electric buses also enter the routes. These vehicles, due to the presence of heavy traction batteries mainly on the roof, have a tendency to an increased roll angle and a tendency to roll over. Therefore, there is a need to apply anti-rollover measures for such vehicles. The purpose of the work – development of a law and a control algorithm that allows reducing the torque to reduce the tendency of an articulated electric bus to overturn Materials and methods. During the development and research of the algorithm, the MATLABSimulink simulation environment is used with the developed mathematical model of spatial motion of an articulated electric bus. Results. The derivation of formulas for calculating the critical turning speed of sections for articulated vehicles is presented, an algorithm and a traction control law are formulated depending on the turning parameters, graphs are presented that substantiate the operability and effectiveness of the algorithms. Conclusion. The practical value of the developed algorithm lies in its practical application on an articulated vehicle in order to reduce the tendency to increased roll angles and rollover protection.

Prediction-based controller radial neural network for the traction control system

One of the systems needed to improve vehicle safety is the traction control system (TCS). This control mechanism keeps the wheels from slipping too much when the car accelerates, especially when it moves quickly. Because of the significant nonlinear behavior of the tire during acceleration and the unknown impacts of the road surface, it might be difficult to maintain wheel slip in an acceptable range, especially in bad weather. However, some uncertainties, such as unmodeled dynamics and vehicle parameter uncertainty, should be taken into account when designing the controller. Consequently, TCS requires the existence of a strong nonlinear control law. In this study, an analytical design for a TCS controller is made using the method of nonlinear predictive control. The control system’s resistance is then increased by employing an adaptive radial basis neural network (RFNN) to predict the system’s unknown uncertainties. In this study, the controller was designed using half car and quarter car models, respectively. The behavior of the suggested control system for tracking the reference wheel’s slip in the face of uncertainty for various movements is examined in the simulation results that follow. The resulting results have been compared with the simulation results generated from the nonlinear sliding mode controller response used in valid articles in order to provide a more thorough examination of the suggested control system. The findings demonstrate the effective performance of the suggested control mechanism against nonlinear effects and uncertainties.

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision.

SHIELD: Security-aware Schreduling for Real-Time DAGs on Heterogeneous Systems

Many control applications in real-time cyber-physical systems are represented as Directed Acyclic Graphs ( DAGs ) due to complex interactions among their functional components, and executed on distributed heterogeneous platforms. Data communication between dependent task nodes running on different processing elements are often realized through message transmission over a public network, and are hence susceptible to multiple security threats such as snooping , alteration and spoofing . Several alternative security protocols having varying security strengths and associated implementation overheads are available in the market, for incorporating confidentiality , integrity and authentication on the transmitted messages. While message size and conceptually its associated transmission overheads may be marginally increased due to the assignment of security protocols, significant computation overheads must be incurred for securing the message at the location of its source task node and for unlocking security/message extraction at the destination. Obtained security strengths and associated computation overheads vary depending on the set of protocols chosen for a given message from an available pool of protocols. Given lower bounds on the security demands of an application's messages, selecting the appropriate protocols for each message such that a system's overall security is maximized while satisfying constraints related to the resource, task precedence and deadline, is a challenging and computationally hard problem. In this paper, we propose an efficient heuristic strategy called SHIELD for security-aware real-time scheduling of DAG-structured applications to be executed on distributed heterogeneous systems. The efficacy of the proposed scheduler is exhibited through extensive simulation-based experiments using two DAG-structured application benchmarks. Our performance evaluation results demonstrate that SHIELD significantly outperforms two greedy baseline strategies SHIELDb in terms of solution generation times (i.e., run-times) and SHIELDf in terms of achieved security utility. Additionally, a case study on the Traction Control application in automotive systems has been included to exhibit the applicability of SHIELD in real-world settings.

An Algebraic Estimation-Based Model-Free Control for Acceleration Slip Regulation of Electric Vehicles

An Algebraic Estimation-Based Model-Free Control for Acceleration Slip Regulation of Electric Vehicles

Autonomous countertraction for secure field of view in laparoscopic surgery using deep reinforcement learning.

Countertraction is a vital technique in laparoscopic surgery, stretching the tissue surface for incision and dissection. Due to the technical challenges and frequency of countertraction, autonomous countertraction has the potential to significantly reduce surgeons' workload. Despite several methods proposed for automation, achieving optimal tissue visibility and tension for incision remains unrealized. Therefore, we propose a method for autonomous countertraction that enhances tissue surface planarity and visibility. We constructed a neural network that integrates a point cloud convolutional neural network (CNN) with a deep reinforcement learning (RL) model. This network continuously controls the forceps position based on the surface shape observed by a camera and the forceps position. RL is conducted in a physical simulation environment, with verification experiments performed in both simulation and phantom environments. The evaluation was performed based on plane error, representing the average distance between the tissue surface and its least-squares plane, and angle error, indicating the angle between the tissue surface vector and the camera's optical axis vector. The plane error decreased under all conditions both simulation and phantom environments, with 93.3% of case showing a reduction in angle error. In simulations, the plane error decreased from to , and the angle error from to . In the phantom environment, the plane error decreased from to , and the angle error from to . The proposed neural network was validated in both simulation and phantom experimental settings, confirming that traction control improved tissue planarity and visibility. These results demonstrate the feasibility of automating countertraction using the proposed model.

Acceleration Slip Regulation Control Method for Distributed Electric Drive Vehicles under Icy and Snowy Road Conditions

To achieve a rapid and stable dynamic response of the drive anti-slip system for distributed electric vehicles on low-friction surfaces, this paper proposes an adaptive acceleration slip regulation control strategy based on wheel slip rate. An attachment coefficient fusion estimation algorithm based on an improved singular value decomposition unscented Kalman filter is designed. This algorithm combines Sage–Husa with the unscented Kalman filter for adaptive improvement, allowing for the quick and accurate determination of the road friction coefficient and, subsequently, the optimal slip rate. Additionally, a slip rate control strategy based on dynamic adaptive compensation sliding mode control is designed, which introduces a dynamic weight integral function into the control rate to adaptively adjust the integral effect based on errors, with its stability proven. To verify the performance of the road estimator and slip rate controller, a model is built with vehicle simulation software, and simulations are conducted. The results show that under icy and snowy road conditions, the designed estimator can reduce estimation errors and respond rapidly to sudden changes. Compared to traditional equivalent controllers, the designed controller can effectively reduce chattering, decrease overshoot, and shorten response time. Especially during road transitions, the designed controller demonstrates better dynamic performance and stability.

Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions.

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.

Research on the traction characteristic evaluation of urban rail vehicles based on entropy weight TOPSIS

This paper aims to investigate the influence of different speed at the end of the constant power section of the traction characteristic curve (TCC) on the vehicle dynamic performance and evaluate the TCC. A dynamics co-simulation model is developed in SIMPACK and MATLAB for dynamic analysis of the urban rail vehicle (URV) under different traction characteristics. Then it is used to investigate how acceleration distance and vehicle dynamic performance varies with the speed at the end of the constant power section of the TCC under different starting torques. Moreover, the entropy weight TOPSIS method which is widely used to solve the multi-objective decision-making problem is chosen to evaluate the traction characteristics based on various dynamic performance indexes over 25 test sections and acceleration distance. The results show that under traction conditions, the starting torque has effects on the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces, derailment coefficient and wheel unloading factor. The higher the torque, the greater the value of each index, that means the poorer the vehicle ride characteristics and running safety. For a given starting torque, the dynamic performance indexes increase with the increase of the speed at the end of the constant power section from 50 km/h to 80 km/h during the vehicle speed-up process. The maximum growth rate of the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces are 17%, 8%, 8% and 2%, respectively, when the speed at the end of the constant power section increases from 50 km/h to 80 km/h. Last, the comprehensive evaluation of traction characteristics using entropy-weight TOPSIS method reveals when the starting torque is 800 [Formula: see text], 1000 [Formula: see text], 1200 [Formula: see text] and 1400 [Formula: see text], the corresponding optimal speed at the end of the constant power section is 80 km/h, 80 km/h, 70 km/h and 50 km/h, respectively. The result of the study can provide theoretical support for traction characteristic design and traction control for URVs.

Botulinum Toxin Enhanced Foker Process for Long Gap Esophageal Atresia

BackgroundThe traction-induced esophageal growth (Foker) process for the treatment of long gap esophageal atresia (LGEA) relies on applying progressive tension to the esophagus to induce growth. Due to its anti-fibrotic and muscle-relaxing properties, we hypothesize that Botulinum Toxin A (BTX) can enhance traction-induced esophageal growth. MethodsA retrospective two-center cohort study was conducted on children who underwent a BTX-enhanced Foker process for LGEA repair from 2021 to 2023. BTX (10 units/ml, 2 units/kg, per esophageal pouch) was applied at the time of traction initiation. Time on traction, complications, and anastomotic outcomes were compared against historical controls (Foker process without BTX) from 2014 to 2021. ResultsTwenty infants (LGEA type A:12, B:4, C:4; 35% reoperative; median [IQR] age 3 [2–5] months), underwent BTX-enhanced Foker process (thoracotomy with external traction: 9; minimally invasive [MIS] multi-staged internal traction: 11). Mean gap lengths were similar between BTX-enhanced external and external traction control patients (mean [SD], 50.6 mm [12.6] vs. 44.5 mm [11.9], p = 0.21). When compared to controls, the BTX-enhanced external traction process was significantly faster (mean [SD], 12.1 [1.6] days vs. 16.6 [13.2] without BTX, p = 0.04) despite similar preoperative gap lengths. There was no difference in time on traction for those undergoing a minimally invasive process. There were no significant differences in complications or anastomotic outcomes in either cohort. ConclusionBotulinum toxin may play a role in accelerating the traction-induced esophageal growth process for LGEA repair. Minimizing time on traction can decrease sedation and paralysis burden while on external traction. Further studies are needed to elucidate the effects of BTX on the esophagus. Level of EvidenceLevel III. Type of StudyRetrospective, Two-center, Cohort study.

Structural computational analysis of conventional and self-designed rover wheel architectures for extraterrestrial roving application

The technical advancements in robotics have closely been followed by developments in aerospace excavation robots. This research elucidates self-designed shape memory alloy wheels for their use in Lunar Excavations, which attributes to the new era of space exploration robots. The wheel design proves to be of paramount importance as crucial operations such as mobility, steering, and traction control build upon wheel design. A comparison has been made between Aluminium alloy, Structural Steel, and Shape Memory alloy (Nitinol) wheels for both conventional and self-designed wheel architectures. Wheel architecture tests and their results are articulated with the aid of simulation. The significant performance parameters, primarily Total Deformation, Equivalent Stress, and Shear stress-induced, were analyzed, which articulates the performance of the wheel at the time of landing. It was observed that for both wheel designs: self-designed and conventional, the maximum total deformation was 0.011154 mm and 1.3573 mm, respectively, when Aluminium alloy was chosen as the base material. For Steel and Nitinol, the self-designed wheel structure proved to perform better than the conventional wheel design with lower values of total deformation (0.0041129 mm and 0.010968 mm, respectively).

Time-series traction prediction of surrounding rock deformation in tunnel construction based on mechanical parameter inversion

The deformation of the surrounding rock serves as a macroscopic representation of the stability status of the tunnel, and is also an important indicator for stability evaluation. To address the difficulty of accurately predicting long-term deformation in tunnel surrounding rock with limited monitoring data, a time-series traction prediction method based on the inversion of mechanical parameters was proposed. Employing the adaptive vigilance chaotic sparrow search algorithm (ACSSA) to optimize initial parameters in extreme learning machines (ELM) and long short-term memory (LSTM) neural networks. It then constructed two deformation time-series prediction models and a rock mechanics parameter inversion model based on the ACSSA-ELM and ACSSA-AMLSTM algorithms. Based on the mechanical parameter inversion model and numerical simulation, the value of the surrounding rock deformation at the key construction nodes of the tunnel was calculated as the traction point, and the traction interval and traction control equation were defined. Cubic spline interpolation and wavelet decomposition were employed to preprocess the measured deformation data of the surrounding rock. Subsequently, the proposed time-series prediction model was utilized for prediction. The predicted results within the traction interval undergo correction based on the traction points, thereby achieving a more precise and intelligent prediction of the tunnel surrounding rock deformation. Based on literature cases and data from the Huayang Tunnel project in Chongqing, the proposed method was validated and applied. The model's accuracy has been validated and successfully guided construction and production. Finally, the impact of different methods on traction effectiveness and the distribution pattern of traction points on model performance was discussed.

An NMPC-Based Integrated Longitudinal and Lateral Vehicle Stability Control Based on the Double-Layer Torque Distribution.

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro-mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles.

Estimation of Soil Characteristic Parameters for Electric Mountain Tractor Based on Gauss–Newton Iteration Method

Future field work tasks will require mountain tractors to pass through rough terrain with limited human supervision. The wheel–soil interaction plays a critical role in rugged terrain mobility. In this paper, an algorithm for the estimation of soil characteristic parameters based on the Simpson numerical integration method and Gauss–Newton iteration method is presented. These parameters can be used for passability prediction or in a traction control algorithm to improve tractor mobility and to plan safe operation paths for autonomous navigation systems. To verify the effectiveness of the solving algorithm, different initial values and soils were selected for simulation calculations of soil characteristic parameters such as internal friction angle, settlement index, and the joint parameter of soil cohesion modulus and friction modulus. The results show that the error was kept within 2%, and the calculation time did not exceed 0.84 s, demonstrating high robustness and real-time performance. To test the applicability of the algorithm model, further research was conducted using different wheel parameters of electric mountain tractors under wet clay conditions. The results show that these parameters also have high accuracy and stability with only a few iterations. Thus, the estimation algorithm can meet the requirements of quickly and accurately identifying soil characteristic parameters during tractor operation. A criterion for the passability of wheeled tractors through unknown terrain is proposed, utilizing identified soil parameters.

Preface

The 2023 2nd International Conference on Renewable Energy and Electrical Technology (ICREET 2023) was held from December 22nd to 24th, 2023 in Tianjin, China via virtual form. With ICREET 2023, the conference series International Conference on Renewable Energy and Electrical Technology (ICREET) completed its second edition.ICREET 2023 attracted a number of papers related to renewable energy and electrical technology, including but not limited to: Renewable Energy Power Generation, Clean Production and Green Chemical Technology, Smart Grid Power Transmission and Distribution, Power System Modeling, Simulation and Analysis, Electric Traction Systems and Control, etc.All of the papers were exhaustively reviewed by the program committee members and peer-reviewers, who took into account the breadth and depth of the research topics that fall under the scope of ICREET. The most promising contributions were selected from all the submissions for presentation and inclusion in this paper volume, which presents innovative original ideas or results of general significance supported by clear and rigorous reasoning and compelling new evidence, as well as methods.The Conference agenda fell into mainly three parts, including keynote speeches, oral presentations, and academic investigation. Keynote speeches were performed by three distinguished professors: Prof. Abdul Hasib Chowdhury (Bangladesh University of Engineering and Technology, Bangladesh), whose research interests include power system planning, stability, reliability analysis, contingency and security analysis, power quality analysis etc., talked on Torsional Resonance in Turbine-Generator Shaft Due to the Operation of Multiple Electric Furnaces at Close Proximity, Assoc. Prof. Hidayat Bin Zainuddin (Universiti Teknikal Malaysia Melaka, Malaysia), who has authored and co-authored over 50 technical papers comprising journals and conference proceedings, and contributed in reviewing articles for many conferences and journals published by IEEE, Elsevier, etc., on Ester Oil as Greener Insulating Fluid for Oil-Filled Transformers, Assoc. Prof. Norzanah Rosmin (Universiti Teknologi Malaysia, Malaysia), who has 23 years’ experience in electrical engineering courses teaching and supervision of students’ projects and researches, and has published many papers in books, journals and conference proceedings and got numbers of awards for innovation projects based on her expertise, on Energy Efficiency and Energy Management System via Double Layer Approach for A Sustainable Future.We would like to acknowledge all the speakers, authors, Technical Program Committee members and anonymous reviewers for their efforts in making ICREET 2023 so successful. We’d also appreciate the support from the staff of Journal of Physics: Conference Series for making this volume published.The Committee of ICREET 2023List of Committee Member is available in this Pdf.

Fundamental Measurement of Adhesion Characteristics of a Scaled Roller Rig Device to Emulate Fast Change of Adhesion for Evaluating Railway Traction Control Performance

Fundamental Measurement of Adhesion Characteristics of a Scaled Roller Rig Device to Emulate Fast Change of Adhesion for Evaluating Railway Traction Control Performance

Fabrication of Intelligent Braking System

Abstract: The braking system was designed and applied on a car to make the driving process safety using embedded system design. Most of the accident occurs due to the delay of the driver to hit the brake, so in this project work braking system is developed such that when it is active it can apply brake depending upon the object sensed by the Proximity sensor and speed of vehicle. Currently, vehicles are often equipped with active safety systems to reduce the risk of accidents, many of which occur in the urban environments. The most popular include Antilock Braking Systems (ABS), Traction Control and Stability Control. All these systems employ different types of sensors to constantly monitor the conditions of the vehicle, and respond in an emergency situation. An intelligent mechatronic system includes an Proximity wave emitter provided on the front portion of a car producing and emitting Proximity waves frontward in a predetermined distance. An Proximity receiver is also placed on the front portion of the car operatively receiving a reflective Proximity wave signal. The reflected wave (detected pulse) gives the distance between the obstacle and the vehicle and RPM counter gives speed of vehicle. The microcontroller is used to control the braking of the vehicle based on the detection pulse information to push the brake pedal and apply brake to the car stupendously for safety purpose.

Preface

It is a pleasure to welcome you to the 2023 8th International Seminar on Computer Technology, Mechanical and Electrical Engineering (ISCME 2023). At this dynamic and innovative time, we were honored to invite academic elites from all over the world to discuss the latest advances and cutting-edge research in computer technology, mechanical and electrical engineering.ISCME 2023 was held in Nanjing, China from 17th to 19th November 2023 (hybrid event). It focused on cutting-edge issues in computer technology, mechanical and electrical engineering, and provided a platform for academia to share knowledge, exchange views and promote collaboration.The Conference has invited renowned scholars and experts from around the world, who brought us a series of fascinating thematic presentations. These reports covered multiple aspects of computer technology, mechanical and electrical engineering, including but not limited to hot topics such as Artificial Intelligence, Big Data Analytics, Smart Grid, Electrical Traction Systems and Controls, Mechanical and Electrical Integration, etc. We believe that these reports have provided participants with insight and inspiration, and promoted research and development in related fields. In addition, it featured oral presentations and poster presentations, providing an opportunity for researchers to present their research findings. It was an important platform for participants to exchange and interact with each other, helping to share experiences, promote cooperation and build new partnerships.The aim of ISCME 2023 was to promote interdisciplinary collaboration and innovation in the fields of computer technology, mechanical and electrical engineering. We believe that through the interaction and cooperation of this Conference, we have deepened our understanding of these fields, promoted interdisciplinary integration, and provided new solutions to the complex problems facing the world today.Finally, we believe that the proceedings of this conference will be of great significance to the academic community. The proceedings features outstanding papers from ISCME 2023, which represents the latest research results and innovative ideas. By publishing and sharing these papers, we are able to further expand the impact of our research, promote communication and collaboration among the academic community, and provide valuable references for future research.We would like to acknowledge all those who supported and participated in ISCME 2023. We look forward to have your active participation in future series conference of ISCME!The Committee of ISCME 2023List of Committee Member is available in this pdf.

Distributed virtual simulation experimental software for high-power electric traction system of 600 km/h high-speed maglev train

The operational speed of high-speed maglev trains has reached a groundbreaking 600 km/h, facilitating faster intercity transportation. However, the design and verification of corresponding high-power electric traction systems face significant challenges. To address these issues, this study develops a distributed virtual simulation experimental software for the high-power electric traction system of a 600 km/h high-speed maglev train. The software first establishes virtual digital models for the high-speed maglev train, the high-speed maglev guideways, and the high-power electric traction converters to simulate the high-speed maglev line operation scenarios. Next, it constructs a client–server simulation software architecture, designs a human–machine interaction platform and an electric traction mathematical module, and devises an information exchange method to enable real-time tracking and display for the operating conditions of high-power electric traction systems. Lastly, by integrating electric traction control strategies, fault analysis and optimization design modules, the software effectively designs and verifies the optimal electric traction systems of high-speed maglev trains, and promptly identifies the potential faults in long stator linear synchronous motors and high-power electric traction converters within the electric traction systems. Engineering applications demonstrate that the software can significantly enhance the design and virtual simulation efficiency for the high-power electric traction systems of high-speed maglev trains, thus meeting critical engineering application needs.