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.
