Running rails are employed as the reflux conductor in DC metro systems, whose longitudinal resistance and joint resistance usually increase with the operating years of the system. Subsequently, the partial backflow obstruction occurs, which will greatly increase rail potential (RP) and endanger the security of the metro line operation. In this paper, a dynamic calculation model of DC metro systems with coupled multiple reflux paths is proposed to study the effect and control method of partial backflow obstruction. Firstly, the dynamic simulation model with coupled multiple reflux paths of DC metro is proposed, and the equivalent chain circuit and unified node admittance matrix are established with partial backflow obstruction. Secondly, the effect of backflow obstruction in DC metro systems is studied for the first time with multiple trains operating in Guangzhou Metro Line 2. Thirdly, the control method of partial backflow obstruction with parallel low-resistance reflux conductors is proposed, and the effectiveness of the control method is analyzed. Results show that partial backflow obstruction will seriously raise the dynamic RP in nearby sections, and the parallel low-resistance reflux conductors can effectively restrain the influence of backflow obstruction.
In the era of rapid transportation electrification, the integration of power system automation and transportation system networking promotes the construction of comprehensive integration of the highway industry. Due to the huge project and the difficulty of construction, traditional highway construction cannot fully consider the supporting construction of electrification and networks and often lays monitoring and network facilities near the power facilities around the path. With the maturity of photovoltaic and other new energy technologies, and considering the increasing demand for modern highways, we put forward the design requirements and transformation standards of energy-supporting facilities. Based on the whole path, the power-supporting projects are realized to establish a new highway project with electrification, networking, and whole-process monitoring. This study addresses the issue of supply-demand mismatch in photovoltaic energy by proposing an integrated nonlinear regression framework based on the Transformer architecture to impute missing data. Considering the impact of energy storage conversion energy loss on the calculation effect of the model, and the input unit extracts the global degradation external characteristics, the long linear regression model is used to design the potential of recurrent neural network (RNN) shallow feature extraction. Multiple parallel RNN-transformer (RNN-T) are set to extract the external characteristics of power supply and demand balance without sharing the relevant parameters. The experimental process in the simulation platform built has verified that the model established in this study reduces the complexity and eases the accuracy error caused by data calculation. While in the matching ability and prediction accuracy, the test results are significantly better than the traditional method because its overall performance is improved by 25.2% and 29.7%, respectively, which supports that the improved model can learn the relationship between multilayer heterogeneous data simultaneously. Therefore, it improves the model's deep learning ability in the photovoltaic power supply and demand relationship model. High-precision prediction effect is also achieved, which has great market value and practical significance for future road development.
Direct current (DC)–DC converters are the backbone of electric vehicle (EV) power trains, enabling efficient and bidirectional energy flow between the battery, high-voltage (HV) DC link, and auxiliary rails under tight isolation, gain, ripple, and electromagnetic interference (EMI) constraints. This review catalogs 29 EV DC–DC converter families and classifies them by isolation requirement, gain window, bidirectionality, and ripple tolerance. On the implementation side, we survey modern control-digital voltage/current-mode (VMC/CMC), model-predictive/model-free, and artificial intelligence (AI)–assisted scheduling-together with protections (device level desaturation/soft turn-off through contactor policy) and thermal/EMI codesign (hot-loop minimization, spread spectrum, common-/differential-mode filtering). Normalized benchmarks at three representative operating points-auxiliary 12–48 V, mid-rail 48–800 V, and traction 350–1000 V show dual active bridge (DAB) and inductor–inductor–capacitor/capacitor–inductor–inductor–capacitor (LLC/CLLC) at 96%–98% for isolated HV links and on-board chargers (OBCs), interleaved buck–boost and multidevice interleaved bidirectional converters (MDIBCs) at 97%–98% for nonisolated bus ties, and multi-input power electronic converters/boost stages (PECs/boost) at 92%–96% with superior source decoupling. We provide a selection matrix mapping battery EV/hybrid EV/plug-in hybrid EV/fuel cell EV (BEV/HEV/PIHEV/FCEV) functions to topologies, a grid-to-vehicle ↔ vehicle-to-grid (G2V↔V2G) mode transition procedure, and 2020–2025 updates (silicon-carbide/gallium-nitride [SiC/GaN] devices, single-/triple-phase shift (SPS/TPS) and variable-frequency scheduling, and predictive/digital control). No single converter is universally optimal; choices balance isolation and gain against ripple limits, control complexity, EMI/thermal budgets, and cost.
Linear induction motors are in vogue these days. High-speed electromagnetic propulsion powered by three-phase pulsed power is increasingly sought in emerging industrial projects, such as hyperloop transportation, factory automation for heavy machinery and defence applications that require rapid acceleration of heavy masses, including launchers and electromagnetic aircraft launch systems (EMALS). These energy applications require ensuring reliable and error-free operation under dynamic conditions. The Double-Sided Linear Induction Motor (DSLIM), featuring a long primary and a short secondary that incorporates distributed winding, exhibits oscillatory behaviour in its dynamic thrust-speed characteristics, as well as the occurrence of zero or negative thrust during acceleration, resulting in delayed movement progress of the mover. These effects can be mitigated by optimising the mover material and winding configuration. Case studies of 21-m and 90-m long-primary DSLIMs, housing tooth windings with German silver as a conducting sheet, comprising the secondary mover, were conducted to accelerate a heavy mass to high speeds required for the above-mentioned applications. A double-layer DSLIM with a variable-voltage-variable-frequency supply is analysed, and prototype experimental results for a 1.5 m configuration are compared to a double-layer three-phase tooth winding with distributed winding.
The accurate estimation of the probability distribution of traction current harmonics in EMUs is crucial for preventing electromagnetic interference (EMI), managing high-speed railway signaling systems, and analyzing traction system power quality. Although kernel density estimation (KDE) has been a longstanding flexible, nonparametric estimation method, it faces significant challenges, including high computational demands and difficulties in selecting appropriate bandwidths. This article introduces the Gaussian mixture model (GMM) as a novel approach for the probabilistic estimation of traction current harmonics, thereby providing a scientific foundation for harmonic analysis. To improve the precision and efficiency of harmonic interference assessments, we initialize the GMM parameters and the expectation-maximization (EM) algorithm with the density-based spatial clustering of applications with noise (DBSCAN). The effectiveness of our method is confirmed through the Kolmogorov–Smirnov (K–S) goodness-of-fit test, root mean square error (RMSE), and R 2 statistics, demonstrating that our approach provides greater stability and faster computation than existing methods.
In addressing the issue of electromagnetic interference in railway environments, research into the coupling pathways, amplitude distribution patterns, and attenuation characteristics of such interference is crucial for identifying practical solutions. Rails, a common channel for signaling and traction power supply systems, play a pivotal role in anti-interference design. However, due to their ferromagnetic material properties and irregular H-shaped cross-section, accurately defining their frequency-dependent impedance has been challenging, leading to inaccuracies in the electrical modeling of railway stations. This paper proposes a segmented modeling approach for rails, analyzing and deriving the internal impedance calculation formulas based on the distinct shapes of each rail segment. Taking the working mode of track circuits as an example, the influence of external circuit loop impedance is considered, culminating in the development of a computational model for the frequency-dependent impedance of rails. Using the 60 kg/m rail as an example, the model’s impedance calculation accuracy at specific frequency points deviates by less than 6% from standard errors. Additionally, leveraging the ANSYS platform, the finite element simulation method was employed to simulate the frequency-dependent internal impedance of rails below 100 kHz. The results showed a high degree of agreement with the internal impedance parameters derived from the model, thereby validating the model’s accuracy within the frequency range below 100 kHz under long rail conditions. This model enhances the precision of station modeling, particularly in improving the accuracy of interference amplitude distribution and attenuation characteristic calculations.
The rapid adoption of electric vehicles (EVs) poses new challenges for both transportation networks and power distribution systems. To address these issues, effective EV routing strategies are essential to minimize grid stress and ensure efficient energy utilization. This research proposes an optimal EV routing by incorporating user-centric parameters and coordination of demand response management (DRM) with distributed generation (DG), facilitating effective synergy between user preferences and grid operational reliability. A loopless route is formulated considering distance and travel time (TT) to minimize the routing cost using Yen’s algorithm (YA). The optimized route, integrated with DG–DRM, ensures the minimization of power loss (PL) and the maximization of customer benefit (CB) using population-based incremental learning (PBIL) algorithm. To enable effective coordination between EV routing and DRM in the distribution network, the Monte Carlo sampling method is employed to validate stochastic traffic and load variations. IEEE-33 bus and the Indian utility power system (IUPS) network comprising 17 busses, are taken as test systems. The proposed methodology is compared with other soft computing techniques, and the findings demonstrate its superiority by achieving a 12.3% reduction in routing cost (RRC), a 16.74% reduction in PL, and a 22.31% increase in CB.
Due to their nonbackup characteristics and constant exposure to outdoor conditions, the performance of overhead contact lines (OCLs) will gradually degrade over time and further result in equipment defects or frequent failures. These issues significantly impact system availability and incur substantial repair costs. To tackle these issues, this paper proposes a stochastic colored Petri net (SCPN) model to evaluate the availability of OCLs and estimate the maintenance costs, simultaneously simulating the degradation, failure, inspection, and maintenance processes of critical components and the overall system. Firstly, this model encompasses the nature of a multiple-stage deterioration process and various maintenance actions available for OCLs. A four-state transition diagram is developed to capture the intricate dependencies involved. Moreover, a subnet is formulated using SCPN to represent the four-state transition for critical components, which are described by nine tuples. Additionally, a system model is developed by integrating the subnets of OCL components. To improve simulation speed, an accelerated Monte Carlo simulation algorithm is devised to handle the analytical solution for the complex integration associated with performance transitions. Finally, the proposed approach is demonstrated by its application to an actual high-speed railway line, showcasing its effectiveness in addressing the degradation and maintenance challenges of OCLs.
In the prediction of traction loads for electrified railways, conventional forecasting methods often focus exclusively on temporal correlations within historical data from individual substations. However, traction loads are profoundly affected by train schedules and exhibit substantial spatial interdependence across different substations. To address this limitation, this study proposes a hybrid model integrating a graph convolutional network (GCN) and a bidirectional long short-term memory (BiLSTM) network, which comprehensively incorporates both spatial and temporal dependencies to significantly improve ultrashort-term prediction accuracy. The proposed framework operates in several stages. First, spatial correlations among regional substations are captured using a GCN. To mitigate the risk of including spurious connections—often referred to as “pseudo-adjacency” relationships—the adjacency matrix is refined using Pearson correlation coefficients, thereby strengthening the model’s representation of meaningful spatial interactions. The spatial features extracted by the GCN at consecutive time steps are then organized into a temporal sequence and input into the BiLSTM module. To further enhance temporal modeling, an attention mechanism is incorporated to adaptively weigh the importance of hidden states, enabling the model to focus on the most relevant temporal information. This integrated approach results in a notable improvement in the accuracy of traction load power forecasting. Results from case studies demonstrate that the proposed model, with appropriately configured spatiotemporal parameters, achieves superior prediction accuracy. This finding underscores the necessity of incorporating spatiotemporal characteristics for traction load forecasting.
This study presents a dual-active-bridge (DAB) LC resonant DC-DC converter for battery energy storage systems. The proposed converter adds an auxiliary bridge arm to the traditional full-bridge (FB) LC resonant converter as a boost arm. It features two efficient operating modes: a charging low-gain (CLG) mode and a discharging high-gain (DHG) mode. In CLG mode, the converter operates as an FB resonant PWM converter to achieve step-down functionality. In DHG mode, the boost arm is used for energy storage to increase the voltage gain. Both modes enable soft-switching operation across the entire load range. Additionally, the converter operates at a fixed switching frequency, simplifying the design of magnetic components. The converter has little magnetizing current and circulating current to increase the efficiency. The resonant capacitor in the converter has lower voltage stress and the transformer without air gap has lower leakage magnetic field, contributing to high power density. A prototype was developed, with batteries voltage of 40-60 V and high voltage DC bus of 360 V. Experimental results validate the feasibility of the proposed converter.
In order to improve the electromagnetic torque and stability of the drive motor for belt conveyors, and considering economic benefits, two ferrite assisted double-layer nonuniform Halbach array consequent-pole (DNHC-permanent magnet synchronous motors[PMVM]) are proposed: DNHC-PMVMA and DNHC-PMVMB. The main magnetic pole of DNHC-PMVM rotor adopts DNH rare earth permanent magnet, and ferrite is used as the auxiliary magnetic pole of stator and rotor. The rationality of the proposed structure is verified by comparing and analyzing PMVM, DNHC-PMVMA, and DNHC-PMVMB by finite method. In order to further optimize the motor structure, the cuckoo search (CS) grey wolf optimization (CSGWO)algorithm is improved, and the improvement strategies such as circle chaotic mapping are introduced. After multiobjective optimization test, it is proved that the comprehensive performance of improved CSGWO (ICSGWO) is better than that of CSGWO, GWO algorithm, particle swarm optimization (PSO), and other algorithms. Based on the response surface method (RSM), ICSGWO, and parameter scanning, the three motor structures are optimized, respectively. The finite element method is used to analyze the three optimized motors. The results show that the performance parameters such as electromagnetic torque and torque ripple are significantly improved, which verifies the effectiveness of the optimization method. Meanwhile, the performance parameters of DNHC-PMVM are significantly better than those of PMVM, which proves the superiority of the proposed structure.
This article discusses voltage level modifications in urban mass transit traction substations, focusing on DC railway substations, to reduce power consumption and improve energy efficiency. Substation voltage settings are usually adjusted by a skilled designer using practical judgment and design acumen. To maximize operations, all traction substation voltage levels are automatically adjusted to the same value. This arrangement works well and may not have affected the power supply system. This design often causes operations to deviate from optimal performance, perhaps reducing energy efficiency. This research seeks to determine the optimal traction substation voltage setting that minimizes total energy consumption of DC electric railways. A simulation-based approach is applied using train movement data and voltage variation scenarios. The proposed designs are linear, V-shaped, and fixed-voltage. Additionally, particle swarm optimization (PSO) is an effective way to find the best design. The Bangkok Transit System (BTS) Sukhumvit line is used for testing. Reduction by the linear framework, energy consumption may be 2.341% lower than the base case. By the PSO, the results in 30 trial test runs suggest a 6.107% energy consumption reduction from baseline.
This article introduces simultaneous control of oscillations in voltage and frequency within a single-area power system that includes hydrogen energy and electrical vehicles as source. The study focuses on the critical roles played by Automatic Voltage Regulator (AVR) and Automatic Generation Control (AGC) loops in maintaining frequency and voltage stability. The article incorporates renewable energy sources (RESs) in this investigation, like photovoltaic (PV) systems, fuel cells (FCs), and aqua electrolyzers (AEs) into the power grid. Energy storage and electric vehicle integration have also been included in the research to see how they affect the reduction of frequency and voltage oscillations. This study also examined the impact of communication time delays (Tds), which may be the cause of system instability in real-power systems. The proportional integral derivative (PID) controller is selected as a subsidiary controller for the combined study of AGC and AVR, and its efficacy in terms of operation is contrasted with classical I and PI controllers and other control techniques from the literature. A recently developed Secretary Bird Optimization (SBO) algorithm is selected for obtaining the parameters of the controller. This article contributes valuable insights into power system stability enhancement.
Electric road systems (ERSs) are anticipated to be major energy consumers. The energy efficiency of an ERS can be significantly improved by implementing the practice of driving electric vehicles (EVs) in closely spaced platoons. This driving configuration effectively reduces the drag coefficient of all vehicles within the platoon, resulting in a substantial decrease in the power demanded from the grid. Moreover, it enables the collective recuperation of regenerative energy from braking EVs rather than feeding the individual braking energy into each vehicle battery. Recuperating energy is well understood from trains. To safeguard the network from overvoltage, braking resistors are commonly utilised in conjunction with a nearby energy storage system (ESS) or feeding power upstream into the AC grid via bidirectional substations. This paper utilises Simulink to model an ERS featuring two EV platoons (EVPs), simulating power flow within the system and assessing various technologies for regenerative energy recuperation. A control technique for efficient management of regenerative energy is introduced and validated through experiments by using dedicated software designed for emulating regenerative braking energy in DC railway applications.
Electric vehicles (EVs) present an efficient solution for reducing greenhouse gas (GHG) emissions and enhancing grid power quality. They offer multiple advantages over traditional internal combustion engines (ICEs), including lower emissions, reduced dependance on oil, higher energy efficiency, quieter operation, zero emissions, and improved air quality by minimizing the release of toxic chemicals into the atmosphere. However, there is a lack of literature that comprehensively reviews the factors that can facilitate the assimilation of EV technology. Therefore, this paper provides a comprehensive review of EV technologies, focusing on the growth of global EV adoption and the various types of EVs, including all-EVs and hybrid EVs (HEVs). The comparative analysis of different HEV technologies is presented, covering full HEVs, mild HEVs, and plug-in HEVs (PHEVs). The paper also discusses the different classifications of HEVs based on electrification level and energy source, along with a comparative analysis of their configurations. Furthermore, the EV architecture is examined, with a specific focus on electric motors, battery management systems (BMSs), batteries, and charging technologies, including conductive and wireless charging systems. The challenges in EV charging and the associated charging standards are also addressed. The paper concludes by highlighting the need for the advancement of EV technologies and infrastructure to overcome the significant barriers to rapid EV adoption, while demonstrating how smart grid technologies enhance EV charging efficiency, grid resilience, and energy sustainability.
The concept of more electric aircraft (MEA) is a major trend in the aircraft industry. Compared to the conventional aircraft electrical power system (AEPS), the MEA-EPS has become more integrated and complex. The MEA-EPS demonstrates typical characteristics of a cyber-physical system (CPS) as a result of the implementation of intelligent management and information sensing techniques, thereby transforming into an aircraft cyber-physical power system (ACPPS). However, the improved architecture provides reliability while also introducing vulnerability. The methodologies used to evaluate the reliability of conventional aircraft EPS are not easily transferable to ACPPS. Therefore, it is essential to assess the vulnerability of MEA-EPS for stable operation and optimal system design. To identify the critical components and branches of MEA-EPS, this paper proposes an ACPPS framework and a modeling approach. Additionally, by applying complex network theory, the system is abstracted into an undirected network. The statistical properties of the network are examined from both structural and functional perspectives, revealing that the system exhibits a robust scale-free characteristic. Finally, four attack strategies are used to simulate random failures and malicious attacks. Simulation results indicate that the cyber-side is more fragile than the physical-side and several countermeasures are recommended to defend against attacks.
Decarbonizing rail transport in response to global warming is fundamental to achieving a net zero transportation system. Along with increased passenger mobility and network electrification initiatives, significant connectivity between the train and infrastructure is needed to underpin operational communications and safety, information exchange, and customer comfort. Track-to-train data connectivity solutions that are being proposed require a source of electrical power available at regular locations. This source of electricity is not always readily accessible along the railway track, even when the traction systems are powered by electricity. For low-power and low-voltage (LV) applications, deriving electric power from the overhead catenary system is costly, potentially bulky, complicated, or not even technically feasible with present conventional or innovative power derivation methods. This paper investigates the technical feasibility and applicability of the capacitive divider technology in electrified AC traction systems and proposes a power supply solution that could utilize the in situ 25 kV AC overhead line to supply low-power LV applications. A prototype has been developed, and the principles of deriving active power up to 47 W at 108 V have been demonstrated through laboratory experiments and simulations. The prototype has a relatively low complexity, does not require any auxiliary power supply circuitry, has relatively a lower cost compared to other solutions, and can be constructed rapidly due to the availability of off-the-shelf components. The proposed power supply solution has the potential to support data connectivity applications thus becoming an enabler of the information exchanged between train and infrastructure.
Wireless power transfer (WPT) has been developed as a transformative alternative to traditional plug‐in charging for electric vehicles (EVs), offering significant developments in mobile charging. EVs are charged while moving on the roads. This review provides a comprehensive overview of various WPT technologies, including inductive power transfer (IPT), resonant inductive transfer, capacitive power transfer (CPT), microwave power transfer (MWPT) and laser power transfer (LPT), for both near‐field and far‐field applications. Different WPT topologies, such as series–series (SS), series–parallel (SP), parallel–parallel (PP), parallel–series (PS), LC‐S, LC‐P, S‐SP and LC‐LC, are analysed for their specific advantages in EV applications. Additionally, key standards for WPT, including SAE J2954, IEC 61980, ISO 19363, IEEE C95.1‐2345 and TA‐15, are providing a regulatory framework for safe and efficient implementation. The paper also explores the integration of artificial intelligence (AI) techniques like deep Q‐network (DQN) and large language model (LLM) in the WPT system. Further, smart road technologies and cybersecurity measures in WPT systems, with a particular focus on issues such as data protection for cyberattacks, are discussed. The role of the Internet of Things (IoT) and edge computing in monitoring and controlling EVs for optimal charging is discussed. Furthermore, the application of blockchain technology in WPT is discussed. The advancements in coil design are also discussed. Finally, the challenges and limitations of WPT, such as energy transfer efficiency, misalignment of coils, electromagnetic interference (EMI), safety and security, are discussed.
For hybrid buses equipped with hybrid energy storage systems, it is crucial to thoroughly evaluate and analyze the potential of different hybrid configurations in order to select an appropriate powertrain configuration for subsequent development. Currently, due to the low efficiency of energy management systems' multiobjective weighting algorithm and the comparison of different configuration performance under specific component parameters, it is difficult to fully demonstrate the performance potential of configurations. To solve the above problems, multiple allowable component parameter schemes need to be considered. This article introduces a multiobjective evaluation method for hybrid powertrain configurations based on the nondominated sorting dynamic programming algorithm and employs parameter selection and collaborative energy management strategy optimization to overcome the challenge of large computational volume. Through the multiobjective Pareto front of fuel economy and battery SoH (state of health) changes, an effective comparison and analysis of the performance of two hybrid powertrain configurations were conducted. The results reveal that, for an 18-ton urban bus, under equivalent variations in battery SoH, the power-split configuration demonstrates an average fuel consumption reduction of 10.7% compared to the series-parallel configuration. The comparison results of various parameter configuration combinations indicate that the power-split configuration outperforms the series-parallel configuration in urban driving conditions. The aforementioned conclusions also provide reference for the selection of configuration schemes for similar types of commercial vehicles.
Electric vehicles (EVs) are increasingly gaining popularity due to zero greenhouse gas emissions and some other privileges. However, limited battery capacity and drive range are known as the main obstacles to the widespread usage of EVs. Signalized intersections are among the bottleneck situations, in which the transportation systems are away from their optimal operation point. Eco-driving is an effective solution for dealing with idle stop-and-go issue before the signalized intersections, which is attainable thanks to the merits of advanced connected vehicle (ACV) technology. In this study, an eco-cooperative driving strategy is presented in proximity to the signalized intersections, considering driver comfort. First, Virginia Tech's microscopic (VT-Micro) model is developed, taking into account road slope and regenerative braking energy. Then, using the model, all optimal acceleration and deceleration levels for uphill, flat, and downhill scenarios with consideration of driver comfort are determined. Finally, the effectiveness of the eco-cooperative driving strategy is examined.
