Electric Multiple Units Research Articles

High-Capacity Energy Storage Devices Designed for Use in Railway Applications

High-Capacity Energy Storage Devices Designed for Use in Railway Applications

Multi-parameter optimization based on a surrogate model for improving vehicle dynamics and reducing wheel wear in high-speed EMUs

With the continuous development of China's high-speed railways, the problems of vehicle dynamics and wheel wear in high-speed electric multiple units (EMUs) are becoming increasingly prominent, and the suspension parameters of these vehicles have a significant effect on improving vehicle dynamics performance and reducing wheel wear. This paper first establishes a vehicle dynamics model using the Ultra-Latin Hypercube sampling method to select suspension parameters, and targets the improvement of vehicle dynamics performance and the reduction of wheel wear, and finally a Kriging Surrogate Model-Compression Particle Swarm Optimization (KSM-CPSO) model used to optimise the suspension parameters. The vehicle dynamics and wheel wear of the before and after optimisation are analysed. The results showed that the use of optimised suspension parameters effectively increased the vehicle's critical speed. The optimised parameters further improved the vehicle's ride index, safety index, and reduced the lateral force of the wheel axle. In addition, the optimised parameters suppressed the lateral vibration of the vehicle. When the wear mileage reached 200,000 km, the wheel wear depths before and after optimisation were 1.086 and 0.9806 mm, respectively. Therefore, the optimisation of the suspension parameters can effectively improve the vehicle dynamics performance and subsequently reduce wheel wear.

Contact Stiffness Identification Method for Joint Surface and the Effect of Contact Stiffness on the Dynamic Response of the Cable Bracket

The cable bracket is one of the crucial components of high-speed Electric Multiple Units (EMUs), which is fixed to the axle box by bolts to support the cable. As an important parameter of the joint surface, contact stiffness significantly affects the dynamic response and service life of the cable bracket. However, it is difficult to directly measure the contact stiffness of the joint surface. As a result, an identification method is proposed in this investigation to identify the contact stiffness. A finite element model incorporating both normal and tangential stiffness parameters is developed. According to the finite element model and modal testing results, an improved particle swarm optimization algorithm is employed to identify the contact stiffness of the joint surface. The effect of the contact stiffness on the vibrations of the cable bracket is investigated. The findings indicate that pre-tightening torque significantly influences the contact stiffness of the cable bracket. The contact stiffness has a great effect on the vibrations of the cable bracket. Optimal contact stiffness notably reduces vibrations of the cable bracket, thereby extending its service life.

PB-MNSGA-II algorithm and its application for multi-objective optimization design of under-vehicle equipment

Structural damage caused to under-vehicle equipment under adverse working conditions affects the safe operation of high-speed electric multiple units (EMUs). To address the low optimization efficiency and take into account vibration fatigue in traditional under-vehicle equipment design, herein, a multi-objective optimization design method (PB-MNSGA-II) that combines particle swarm optimization-back propagation (PSO-BP) neural network and modified non-dominated sorting Genetic Algorithm II (MNSGA-II) was proposed. It can help engineers choose better equipment component thickness and achieve efficient and accurate design. First, MNSGA-II, the core content of PB-MNSGA-II, was proposed, which improved four aspects of NSGA-II: Latin hypercube sampling population initialization, adaptive crossover and mutation probability, mutation interference of Levy flight strategy, and optimal elite strategy. Then, zero-ductility transition (ZDT) series functions were employed to test MNSGA-II, thereby verifying its convergence and computational efficiency. Subsequently, utilizing the frame structure of the traction transformer (TTR) as an illustrative example, the PB-MNSGA-II approach was employed to enable multi-objective optimization. During this optimization process, the focus was on random vibration fatigue damage, thus enhancing the rationality of the optimization outcomes. After structural optimization, the mass of the TTR decreased by 20.1 kg, and the maximum vibration fatigue damage value decreased from 1.09 to 0.808, verifying the effectiveness and feasibility of the proposed method. The findings of this study provide important insights for the multi-objective optimization design of under-vehicle equipment under complex operating conditions.

Analysis of Regenerative Braking Energy Utilization in KfW i9000 Electric Trains on the Palur-Yogyakarta Operation

Abstract This study explores the potential assessment. of regenerative braking energy in the operation of Electric Multiple Units (EMU) on the Yogyakarta-Palur railway line, a 65.03 km. Prompted by a noticeable increase in passenger numbers in the Yogyakarta metropolitan buffer zone, the study calls for future energy management strategies, involving regenerative braking to reduce energy consumption. Utilizing a mathematical model implemented via MATLAB Simulink, the research encompasses complex train movement scenarios, considering variables such as acceleration values of 0.558 m/s2 and 1 m/s2, and deceleration values of -0.555 m/s2 and 0.36 m/s2. Speed limitations, travel patterns, and energy profiles are rigorously examined, along with incline influences, such as tangent angles of 0.0032 degrees and 0.0017 degrees. In segments like Purwosari-Solo Jebres, electrical energy consumption is quantified at 23.02 kWh, with regenerative braking energy at 1.42 kWh; in Maguwo-Brambanan, consumption is 15.28 kWh, with regenerative energy at 2.42 kWh. The peak power requirements and regenerative braking potentials were also determined, with values such as 619.9 kW and 225.7 kW, and 1785 kW and 198.2 kW respectively. The total electrical consumption for the routes from Palur to Yogyakarta and vice versa was found to be 325.52 kWh and 276.73 kWh, with energy recovery of 31.43 kWh and 26.6 kWh, and average energy recovery ratios of 9.66% and 9.61%.

Hydrogen fuel cell power supply for hybrid electric multiple unit train

In European countries, electrified routes amount for 40% to 65% of the total railway networks length. Some of those routes are only partially electrified, and construction of a catenary network might not be viable on all routes. Consequently, operators run diesel trains under catenary or require both an electric and diesel vehicle, increasing costs of operation. Dual-mode vehicles exist, but they are mostly equipped with diesel generators, adding to the pollution and resulting in reduced movement dynamics. In this article, the authors present a hydrogen-hybrid electric multiple unit (HEMU), as an environmentally friendly vehicle for partially electrified railway lines. Insight into technologies utilized by both hybrid and hydrogen rail vehicles based on the literature review allowed for the formulation of requirements for such a vehicle. Furthermore, an approach to a modelling hybrid vehicle is described, including an energy management algorithm. A series of simulations were conducted, showing an operation of an HEMU on a partially electrified suburban/regional route. The presented simulation results show potential for the future introduction of hydrogen hybrid electric multiple units as a viable solution for partially electrified local and regional routes.

Development and Usability Evaluation of Augmented Reality Content for Light Maintenance Training of Air Spring for Electric Multiple Unit

The air spring for railway vehicles uses the air pressure inside the bellows to absorb vibration and shock to improve ride comfort and adjust the height of the underframe with a leveling valve to control stable driving of the train. This study developed augmented reality content that proposes a novel visual technology to effectively support the training of air spring maintenance tasks. In this study, a special effect algorithm that displays the dispersion and diffusion of fluid, and an algorithm that allows objects to be rotated at various angles, were proposed to increase the visual learning effect of fluid flow for maintenance. The FDG algorithm can increase the training effect by visualizing the leakage of air at a specific location when the air spring is damaged. In addition, the OAR algorithm allows an axisymmetric model, which is difficult to rotate by gestures, to be rotated at various angles, using a touch cube. Using these algorithms, maintenance personnel can effectively learn complex maintenance tasks. The UMUX and CSUQ surveys were conducted with 40 railway maintenance workers to evaluate the effectiveness of the developed educational content. The results showed that the UMUX, across 4 items, averaged as score of 81.56. Likewise, the CSUQ survey score, consisting of 19 questions in 4 categories, was very high, at 80.83. These results show that this AR content is usable for air spring maintenance and field training support.

Investigation on influencing factors of wheel polygonization of a plateau high-speed EMU train

PurposeThis study aims to investigate the cause of high-order wheel polygonization in a plateau high-speed electric multiple unit (EMU) train.Design/methodology/approachA series of field tests were conducted to measure the vibration accelerations of the axle box and bogie when the wheels of the EMU train passed through tracks with normal rail roughness after re-profiling. Additionally, the dynamic characteristics of the track, wheelset and bogie were also measured. These measurements provided insights into the mechanisms that lead to wheel polygonization.FindingsThe results of the field tests indicate that wheel polygonal wear in the EMU train primarily exhibits 14–16 and 25–27 harmonic orders. The passing frequencies of wheel polygonization were approximately 283–323 Hz and 505–545 Hz, which closely match the dominated frequencies of axle box and bogie vibrations. These findings suggest that the fixed-frequency vibrations originate from the natural modes of the wheelset and bogie, which can be excited by wheel/rail irregularities.Originality/valueThe study provides novel insights into the mechanisms of high-order wheel polygonization in plateau high-speed EMU trains. Futher, the results indicate that operating the EMU train on mixed lines at variable speeds could potentially mitigate high-order polygonal wear, providing practical value for improving the safety, performance and maintenance efficiency of high-speed EMU trains.

Fault Diagnosis of the Electric Multiple Unit Door System by Machine Learning Using Sensor Signal of the Simulator

Fault Diagnosis of the Electric Multiple Unit Door System by Machine Learning Using Sensor Signal of the Simulator

Study on the Influencing Factors of Electrical Characteristics of Articulated Split-Zone Insulator Arc

The frequent occurrence of the pantograph–catenary (PC) arc seriously threatens the safe and stable operation of electric multiple units (EMUs). In order to investigate the primary factors affecting the electrical characteristics of the PC arc, this paper first reveals the mechanism of arc generation when EMUs leave the split-zone insulator according to the actual mechanical structure of the PC articulated split-zone insulator. Then, based on the Habedank black box arc model, considering the dynamic changes in arc length during the actual operation of EMUs, a numerical simulation model suitable for describing the PC articulated split-zone insulator arc is established. Furthermore, by comparing with existing research results, the effectiveness of the proposed model is verified. Next, a series of simulation experiments were conducted to analyze the influence of different operating parameters of the EMUs on the electrical characteristics of the PC arc. More importantly, the paper proposes using the entropy weight method to calculate the impact strength of different operating parameters of the EMUs on the arc duration time of the contact wire split-zone region. The research results of this paper can provide some technical references for reducing the erosion of the PC contact wire and slide caused by arcs when the EMUs pass through the split-zone insulator.

Support vector machine-based optimisation of traction gear modifications for multiple-condition electric multiple unit

The traction gear train is subjected to various internal excitations under different traction conditions, such as stiffness excitation, error excitation, meshing shock excitation, and tooth side clearance. The optimal modification scheme is different for each state, and the optimal modification plan based on a single working condition may not be applicable to every working condition. In this paper, we investigate the vibration response characteristics of electric multiple unit gearing under multiple conditions and propose a multi-condition modification scheme. Under different traction conditions, the mapping between gear modification parameters and vibration acceleration in gear transmissions is investigated using support vector machines. Genetic algorithms are used to solve the gear-modifying parameters to minimise the maximum vibration acceleration. A weight assignment principle is proposed to calculate the electric multiple unit traction gear transmission under different conditions, with the operating time and the amount of vibration in each condition as the measurement index. The results of the simulation show that the vibration acceleration under continuous conditions is reduced by 3.55 m/s2, a decrease of 69.88%; the vibration acceleration under high-speed conditions is reduced by 3.301 m/s2, a reduction of 58.74%, and the results show that the overall index of the electric multiple unit traction gear transmission system under different conditions has been improved after the optimisation of the multi-conditions modification, the gear transmission system's vibration is significantly reduced.

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm.

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness.

Research on Fault Prediction Method for Electric Multiple Unit Gearbox Based on Gated Recurrent Unit–Hidden Markov Model

Due to the limited availability of fault samples and the expensive nature of marking fault samples in Electric Multiple Unit (EMU) gearbox monitoring data, a study was conducted to simulate the degradation process of key components in the CRH5 gearbox using rigid–flexible coupling dynamics. Vibration acceleration data from the simulation were utilized to create a six-dimensional hybrid feature domain representing the degradation process. By leveraging the capabilities of the Hidden Markov Model (HMM) for handling hidden transitive probabilities in temporal data and Gated Recurrent Unit (GRU) for addressing long-distance and high-dependence temporal data, a GRU-HMM fault prediction model was developed. This model was validated using monitoring data and the six-dimensional hybrid feature domain from the CRH5 gearbox and compared against actual maintenance records. The findings indicated that the GRU-HMM fault prediction model can effectively recognize the degradation patterns of multiple components, offering higher accuracy in fault prediction compared to traditional models. These research outcomes are expected to optimize EMU maintenance schedules based on usage conditions, enhance EMU utilization rates, and reduce operational and maintenance costs, thereby providing valuable theoretical support.

Mechanics based lateral stability design for a railway Electric Multiple Unit

This paper presented a mechanics-based method to address the typical lateral stability issue of railway trains. A Base Electric Multiple Unit (Base EMU) and an upgraded EMU were used as the research objects. Mechanics-based models were developed for both systems; the Base EMU model was validated using actual test data. Different wheel-rail equivalent conicity values and suspension parameters were investigated. The results showed that inadequate wheel-rail guiding capacity under low conicity can led to the occurrence of lateral stability issues. When the train is pushed by motor cars, coupler lateral forces aggravate stability issues. Altering the yaw damper’s angle and damping properties can significantly reduce lateral swaying and improve the ride index. This represents a low-cost and favored solution. Decreasing the stiffness of the axle box pull rod is also effective in mitigating low-frequency lateral swaying.

Establishment and Analysis of Load Spectrum for Bogie Frame of High-Speed Train at 400 km/h Speed Level

The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs, especially when the design and operational speeds exceed 400 km/h, the applicability of current international standards is uncertain. The load spectrum serves as the foundation for structural reliability design and fatigue evaluation. In this paper, the measured loads of the bogie frame of a CR400AF high-speed train on the Beijing–Shanghai high-speed railway are obtained, and the time-domain characteristic of the measured loads is analyzed under different operating conditions. Then, through the Weibull distribution of three parameters, the Weibull parameters at the 450 km/h speed level are fitted, and the maximum load and cumulative frequency under the speed level are derived. Finally, the load spectrum of the bogie frame at the 450 km/h speed level is established, which provides a more realistic load condition for accurately evaluating the fatigue strength of bogie frames at higher speed levels.

Accurate Identification of Partial Discharge Signals in Cable Terminations of High-Speed Electric Multiple Unit Using Wavelet Transform and Deep Belief Network

Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals.

Influence of Structural Parameters of Gearbox Seal System of Electrical Multiple Units on Seal Performance

The gearbox seal system is a critical component of the electrical multiple units drive system, with a direct impact on the safety and reliability of train operations. This study intends to analyze how structural parameters of the gearbox seal system influence the seal performance from the viewpoint of “oil–air separation and scavenge oil” by using a discrete phase model and volume of fluid. The results show that the oil–air separation performance is mainly affected by the oil droplets’ inertia force and the airflow traction force, and the scavenge oil performance is decided by the oil–air separation performance of each seal chamber. The relationships between seal chamber size, axial seal clearance width, height difference, scavenge oil hole diameter, and seal performance are analyzed, and the study also found that optimizing the stator chamber depth–width ratio of the seal system studied in this paper at 2.5, reducing the rotor chamber depth–width ratio and axial seal clearance width, and increasing the relative height difference can improve the seal performance. And increasing the scavenge oil hole diameter also enhances the seal system’s performance. The simulation results can be used as design references for gearbox seal systems.

Rams-based comprehensive evaluation method for EMU components

EMU (Electric Multiple Units) components were evaluated in multiple dimensions, i.e. reliability, availability, maintainability, and safety. A parameter estimation method, which is based on the data on the mileage between failures and quantities monitored in the actual operation, was put forward for the Weibull proportional hazard model to determine their operational reliability. A multi-index component maintainability evaluation model, which is based on empirical distribution functions, and a maintainability cluster evaluation model, which is based on whitened weight functions, were built, and the entropy weight method was introduced to determine the estimates of the indexes. A component safety evaluation method, which has the safety risk factor as its evaluation basis, was built using both the fuzzy theory and the analytic hierarchy process. A RAMS comprehensive evaluation model based on the linear weighted synthesis method was proposed. All of the models and methods are aimed at meeting the needs of this multidimensional component evaluation. This evaluation method was verified using actual gearbox operation parameters. The reliability and availability evaluation curves based on the Weibull proportional hazard model, whose parameters were determined according to the failure data, show that the EMU component reliability and availability decrease as the EMU mileage increases, thus verifying that this evaluation method is reasonable. The calculated EMU gearbox degree of safety is 0.8659, at an excellent level. The evaluated EMU gearbox maintainability is at the moderate level of III. The proposed evaluation method can reflect the relations of the component reliability and availability to the quantities monitored in operation more accurately and more comprehensively and the actual component maintainability and safety levels in a more objective way. It can provide a theoretical basis for managers and design departments to improve and evaluate the performance of the components, determine their maintenance intervals, and develop scientific preventive maintenance strategies in order to reduce their overhaul cost and increase their maintenance efficiency.

A Reinforcement Learning-Based Pantograph Control Strategy for Improving Current Collection Quality in High-Speed Railways.

In high-speed railways, the pantograph-catenary system (PCS) is a critical subsystem of the train power supply system. In particular, when the double-PCS (DPCS) is in operation, the passing of the leading pantograph (LP) causes the contact force of the trailing pantograph (TP) to fluctuate violently, affecting the power collection quality of the electric multiple units (EMUs). The actively controlled pantograph is the most promising technique for reducing the pantograph-catenary contact force (PCCF) fluctuation and improving the current collection quality. Based on the Nash equilibrium framework, this study proposes a multiagent reinforcement learning (MARL) algorithm for active pantograph control called cooperative proximity policy optimization (Coo-PPO). In the algorithm implementation, the heterogeneous agents play a unique role in a cooperative environment guided by the global value function. Then, a novel reward propagation channel is proposed to reveal implicit associations between agents. Furthermore, a curriculum learning approach is adopted to strike a balance between reward maximization and rational movement patterns. An existing MARL algorithm and a traditional control strategy are compared in the same scenario to validate the proposed control strategy's performance. The experimental results show that the Coo-PPO algorithm obtains more rewards, significantly suppresses the fluctuation in PCCF (up to 41.55%), and dramatically decreases the TP's offline rate (up to 10.77%). This study adopts MARL technology for the first time to address the coordinated control of double pantographs in DPCS.

Simulation Analysis and Experimental Study on the Fluid–Solid–Thermal Coupling of Traction Motor Bearings

The traction motor is a crucial component of high-speed electric multiple units, and its operational reliability is directly impacted by the temperature increase in the bearings. To accurately predict and simulate the temperature change process of traction motor bearings during operation, a fluid–solid–thermal simulation analysis model of grease-lubricated deep groove ball bearings was constructed. This model aimed to simulate the temperature rise of the bearing and the grease flow process, which was validated through experiments. The results from the simulation analysis and tests indicate that the temperature in the contact zone between the bearing rolling element and the raceway, as well as the ring temperature, initially increases to a peak and then gradually decreases, eventually stabilizing once the bearing’s heat generation power and heat transfer power reach equilibrium. Furthermore, the established fluid–solid–thermal coupling simulation analysis model can accurately predict the amount of grease required for effective lubrication in the bearing cavity, which stabilizes along with the bearing temperature. The findings of this research can serve as a theoretical foundation and technical support for monitoring the health status of high-speed EMU traction motor bearings.