Search Results (326)

This paper presents a short-term traction-load forecasting method that fuses optimization-driven dual-scale decomposition and multiscale information fusion (ODE-MMF) with TimeXer-Mamba to address non-stationary prediction difficulties caused by intermittent and volatile traction loads. A correlation analysis module is first constructed for adjacent feeding sections, where mutual information quantifies cross-arm load transfer induced by train operations and extracts key spatial features. In the ODE-MMF signal processing module, an improved whale migration algorithm searches for the optimal parameters of optimization-driven dual-scale decomposition, enabling multiscale decomposition of load features. Multiscale transfer entropy is then used to measure information flow among decomposed components, and highly redundant components are adaptively merged into complementary feature subsequences. In the TimeXer-Mamba prediction module, TimeXer enhances exogenous variables such as holidays, whereas Mamba captures long-range dependencies through the selective state-space model. A gated fusion mechanism integrates the two representations, after which the merged subsequences are predicted in parallel and reconstructed to obtain the final forecast. Experiments conducted on real-world traction-load data demonstrate that the proposed model consistently outperforms all evaluated baselines. Relative to the best-performing baseline, LSTM-Transformer, it achieves reductions of 9.61%, 9.32%, and 9.81% in mean absolute error, root mean square error, and mean absolute percentage error, respectively, while maintaining high computational efficiency and demonstrating strong potential for practical deployment in railway power supply systems. Full article

The objective of the research presented in this paper was to develop methods for simulating the operating conditions of traction power supply systems (TPSSs) equipped with asynchronous generators (ASGs), which may be driven by wind or hydraulic turbines as prime movers, thereby significantly reducing train traction energy costs and lowering carbon monoxide emissions. Using phase-coordinate methods and the Fazonord AC-DC industrial software package, simulations were performed for a TPSS configuration comprising three traction substations (TSs) with ASGs connected to the 6 kV busbars. The results demonstrate that connecting the ASG reduces the maximum active power flow from the utility grid by 27%, decreases peak losses in the 220 kV primary supply line by 44%, and lowers voltage unbalance levels at the 220 kV busbars by 71–76%. Additionally, electromagnetic safety conditions along the 220 kV overhead lines feeding the substations are improved, and the temperature at the hottest points of the traction transformers is reduced. The developed ASG models, implemented using three controlled current sources, are universal and can be applied to TPSSs of various configurations and design layouts. Full article

Rotary tillage–seeding combined operations require stable power take-off (PTO) speed during rotary tillage and accurate tracking of the prescribed travel speed for seeding. Existing energy management strategies for hybrid electric tractors mainly focus on fuel economy and commonly use fixed objective weights, limiting their ability to adjust control priorities under changing operating conditions. To address this issue, an operation-quality-oriented energy management strategy based on model predictive control, termed OQ-EMS/MPC, is proposed. An equivalent combined-operation condition was constructed using the PTO-side rotary-tillage load, drive-side equivalent traction load, segmented travel-speed reference, and equivalent seeding-quality risk. A condition-severity index integrating the PTO-load coefficient of variation, PTO-load impact intensity, and equivalent seeding-quality risk was developed to distinguish steady, fluctuating, and impact-dominated conditions. Based on the identified condition, the weights assigned to PTO-speed regulation, equivalent seed synchronization, and energy economy were adjusted online. These weights were used in the MPC to optimize torque allocation among the engine, motor-generator 1 (MG1), and motor-generator 2 (MG2). The proposed strategy was validated on a dual-side loading bench and compared with a rule-based energy management strategy and a fixed-weight MPC strategy. The overall PTO-speed root-mean-square error (RMSE) was reduced to 1.76 r/min, representing reductions of 58.40% and 45.66% relative to the two comparative strategies, respectively. The equivalent seed-synchronization RMSE was reduced by 69.15% and 52.66%, respectively. Under the impact-dominated condition, the PTO-speed RMSE decreased to 1.65 r/min. The normalized composite cost decreased by 13.53% and 6.26%, while the equivalent fuel consumption increased by 3.40% and 3.76%, respectively. The results demonstrate that the proposed strategy improves PTO-speed stability and equivalent seed-synchronization performance as operating severity increases while accounting for energy economy. Full article

The absence of overhead catenary systems in contactless trams imposes stringent requirements on onboard energy efficiency and real-time power management. Hybrid energy storage systems combining batteries and supercapacitors provide an effective solution; however, conventional energy management strategies often suffer from limited global optimality under frequent traction–braking conditions. To address this issue, this paper proposes a variable-horizon model predictive control (MPC)-based energy management strategy for a battery–supercapacitor hybrid power supply system in contactless trams. A power-level-matching method is first adopted for capacity configuration, and the MPC prediction horizon is then dynamically adjusted to cover the entire traction phase, enabling global energy loss optimization while satisfying voltage, current, and SOC constraints. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed strategy effectively suppresses excessive battery current and premature supercapacitor depletion. Compared with the conventional single-step MPC, the total energy loss is reduced by 9.88%, indicating improved energy efficiency and operational performance. Full article

Reliable state assessment of railway traction power transformers is challenged by heterogeneous operating environments, measurement disturbances, coupled gas-generation mechanisms, and uneven fault-sample distributions. Conventional dissolved gas analysis (DGA) ratio rules and single-model classifiers often show insufficient generalization when rare faults and boundary-ambiguous operating states are encountered. To address this issue, this paper proposes a feature-guided stacking framework for state assessment of oil-immersed railway power transformers. First, a DGA-oriented fusion-feature representation is established by combining raw gas concentrations, gas-ratio descriptors, and an aggregated dissolved-gas analysis factor. Second, DBSCAN-assisted sample structuring is introduced to identify density patterns, sparse rare fault regions, and boundary samples, thereby improving the organization of imbalanced monitoring records. Third, a monitoring-feature-embedded stacking model is developed in which heterogeneous base learners are adaptively weighted according to feature-reliability information and integrated through a cross-validated meta-learner. This synthetic-data-based validation provides a controlled and reproducible proof-of-concept. Therefore, the reported results should be interpreted as evidence of methodological feasibility. Under the default synthetic setting, the proposed feature-guided stacking (FE-stacking) method achieves an accuracy of 99.70% and a macro-F1 of 99.55%. Under the severe minority-retention setting in which only 25% of low-energy discharge (LD) and low-temperature overheating (LT) training samples are preserved, it obtains an accuracy of 99.62%, a macro-F1 of 99.40%, and an LT recall of 96.61%, slightly surpassing random forest (RF) and outperforming Original Stacking in rare fault robustness. These results indicate that feature-guided ensemble learning can improve the generalization stability of DGA-based transformer state assessment under imbalanced and boundary-ambiguous conditions. From a practical perspective, the proposed framework can serve as a decision-support module for transformer condition screening, maintenance prioritization, and alarm verification in railway traction power-supply systems. Full article

This article examines the performance of negative-sequence overcurrent protection during short circuits in the presence of current asymmetry caused by single-phase loads, such as those encountered in AC railway traction systems. The impact of unbalanced loads on the generation of negative-sequence currents is analyzed using field test data and a mathematical model. Various operating modes of an electric power network under unbalanced loading conditions are simulated in MATLAB Simulink R2015a. It is shown that under significant load asymmetry, negative-sequence currents can reach magnitudes comparable to those of short-circuit currents, thereby increasing the risk of false protection operation. To address this issue, a modified negative-sequence overcurrent protection scheme is proposed that ensures both sensitivity and selectivity. The modification is based on analyzing the ratio of negative-sequence to positive-sequence current phasors and monitoring the rate of change of the negative-sequence current. A faulted phase selector is also incorporated into the protection scheme. Simulation results confirm the effectiveness of the modified protection in reliably identifying unsymmetrical short circuits under varying unbalanced load conditions, including remote faults with high fault resistance. Full article

Deep autoencoder-like nonnegative matrix factorization (DANMF) has emerged as a powerful dimensionality reduction paradigm, gaining significant traction in multi-view clustering (MVC) applications. Although existing DANMF-based MVC frameworks demonstrate competitive performance, they remain inherently susceptible to noise contamination and often fail to substantially improve clustering outcomes by effectively exploiting sparse supervisory information. To address these limitations, this paper introduces the correntropy-based semi-supervised multi-view deep autoencoder-like NMF (CSDANMF) framework for advanced multi-view clustering tasks. Compared to conventional DANMF-based MVC approaches, the proposed CSDANMF method introduces two distinct innovations: (1) CSDANMF substitutes the traditional linear Frobenius norm with a non-linear, localized similarity metric—specifically, maximum correntropy—as the loss function, thereby significantly enhancing the model’s robustness against outliers and noise. (2) CSDANMF leverages sparse label information to construct initial pairwise constraints and subsequently deploys a constraint propagation algorithm (CPA) to diffuse these supervisory signals across the data manifold, thereby maximizing the utility of limited prior knowledge to guide the clustering process. Furthermore, we provide comprehensive algorithmic evaluations, including a formal robustness analysis on corrupted datasets and a computational complexity analysis. Extensive experimental results across six benchmark nonnegative multi-view datasets demonstrate that CSDANMF consistently outperforms six state-of-the-art MVC methods, validating its efficacy and superior clustering performance. Full article

This paper presents a battery-aware control framework for a single-phase integrated battery charger (IBC) for electric vehicles, in which the traction system is reused as part of the charging hardware. The proposed charger consists of a stator-assisted bridgeless totem-pole power-factor-correction AC–DC stage and a bidirectional buck–boost DC–DC stage connected to a 48 kWh, 400 V lithium-ion battery pack. The battery pack is modeled using a lookup-table-based equivalent circuit model with state-of-charge- and temperature-dependent open-circuit voltage and impedance parameters. A conventional double-loop PI controller is used as the baseline, while the proposed strategy combines nonlinear model predictive control, an extended Kalman filter, and lookup-table-based battery parameterization to regulate charging current under electrical and thermal constraints. The system is evaluated under 7 kW, 230 V/32 A and 22 kW, 230 V/96 A charging cases using average-model simulations, switching-model transient simulations, and finite element thermal assessment of the induction motor stator. The average-model results show stable charging from 20% to 80% SOC, with charging times of approximately 275 min at 7 kW and 90 min at 22 kW. The EKF provides bounded battery state estimation, with maximum SOC estimation errors of approximately 1.3% and 2.0% for the 7 kW and 22 kW cases, respectively, while the core-temperature estimation error converges close to zero. The switching-model results confirm feasible duty-command behavior, bounded battery-current tracking error, and a representative DC-link ripple of approximately 8 Vpp. During grid-voltage reduction, the charging current is reduced to keep the grid-current envelope within the intended limit. FEM results show that charging-only motor temperatures remain low, reaching approximately 27.39 °C at 7 kW and 38.82–38.85 °C at 22 kW. The most critical charging-related thermal case occurs at 22 kW after one hour of full-load motor operation with a 40 °C initial condition, reaching approximately 92.32 °C. Overall, these simulation-based findings support the feasibility of the proposed NMPC–EKF–LUT framework as a battery-aware supervisory control strategy for single-phase IBC operation. The proposed controller improves constraint-aware, battery state-based decision-making, while switching ripple and motor thermal response are mainly governed by the power stage, feasible current trajectory, and initial thermal condition. Full article

A transient multibody dynamics simulation of rolling-element bearings is the basis for the design of high-speed rotating machinery; however, the established solvers are proprietary, cannot be used with automatic differentiation, and offer no built-in measure of their own physical consistency. We present OpenBEARD, an open-source, fully differentiable transient dynamics solver for angular-contact ball bearings. The solver steps a $40+13Z$-component state (inner ring, cage, and Z balls with quaternion attitude, plus guide-patch, lumped-thermal, and energy-audit states) forward in time under coupled Hertzian contact, Hamrock–Dowson and full-multigrid elastohydrodynamic lubrication, thermal–elastohydrodynamic traction, and centrifugal/press-fit clearance models, using nondimensionalized implicit stiff time integration. A built-in metriplectic conservation audit checks energy closure, the second law per dissipation channel, and the gyroscopic-power identity at every output step. OpenBEARD is cross-verified against two published ADORE references of Gupta. For a high-speed NASA angular-contact ball bearing, the quasi-static contact loads, angles, stresses, and centrifugal force match the published values to within $0.3\%$, and the ball spin and orbital velocities and the spin-axis orientation to ≤0.1%. The inner-race spin-to-roll ratio—a slip-derived secondary quantity that is the most model-sensitive metric in this class of solvers—differs from the NASA quasi-static reference by $8.8\%$. In the separate caged BallBearingTestCase benchmark, the corresponding quasi-static difference is $3.2\%$, and the transient settled value is $\approx 16\%$ above the ADORE step-100 snapshot; these bounded offsets reflect different spin-moment constitutive models. The BallBearingTestCase comparison—a caged bearing under combined thrust and radial load—matches the per-ball contact angles and loads to within $0.23\%$ RMS, and a single published dynamic snapshot (step 100) agrees with the transient contact mechanics to within a few percent. The built-in energy-closure residual stays of order ${10}^{-5}$ with no second-law violations. In the fully transient regime, race control emerges as a dynamical attractor of the coupled traction balance—ball-spin states perturbed by $\pm 12\%$ converge to a single outer-race-control solution—rather than the kinematic hypothesis assumed by quasi-static theory. OpenBEARD is released under the MIT license. Full article

The traction performance analysis of hydro-mechanical transmission (HMT) tractors is employed to evaluate and optimize the transmission system during the design phase, thereby reducing research and development costs associated with continuously variable tractor transmission systems. However, there is currently no established methodological framework for calculating the traction performance of HMT tractors. To address this gap, this study integrated traditional tractor traction performance calculation equations with a self-developed HMT energy consumption calculation method, thereby developing a traction performance calculation model for HMT tractors. Initially, the principle of the tractor’s hydrostatic power-split transmission system was introduced. Subsequently, mathematical models for calculating transmission system energy consumption and tractor traction performance were established, with key sub-models experimentally validated to ensure the reliability of subsequent results. On this foundation, a calculation method for the traction performance of HMT tractors was proposed. Finally, models before and after energy consumption optimization, as well as models under different road conditions and transmission system configurations, were utilized as comparative models to calculate the traction performance of HMT tractors under various settings. The results indicate that energy optimization of HMT not only improves the transmission performance and reduces fuel consumption of HMT tractors, but also enhances the matching capability of HMT tractors with farm tools under high traction efficiency. Additionally, road conditions significantly impact the traction performance of HMT tractors. In wheat stubble fields, the tractor’s traction performance is substantially lower than on standard roads, with a maximum traction force decrease of 40.8% at a slip rate of 30%. Full article

Hairpin windings are increasingly adopted in permanent magnet (PM) traction machines owing to their high slot fill factor, compact end-winding structure, and suitability for automated manufacturing. However, limited heat dissipation and high copper losses under peak loading and high-frequency operation result in severe thermal constraints, which restrict the power rating of the machine. This paper presents a multi-physics comparison of different winding cooling topologies for a PM machine with hairpin winding, including hollow conductor cooling, end-winding cooling, and cooling channel insertion at slot-bottom, slot-middle, and slot-opening regions. A coupled electromagnetic–thermal model based on the finite element method (FEM), which accounts the heat transfer between different components, is used to analyze temperature distribution, losses, efficiency, loading capacity, and hydraulic requirements. The results show that the position of the cooling channel has great influence on the thermal behavior and electromagnetic performance of the machine under different working conditions. The study emphasizes the strong coupling between cooling design, conductor geometry, AC loss behavior, and efficiency and provides practical design guidelines for selecting appropriate cooling techniques in high-power-density traction machines. Consequently, an improved cooling system results in a reduced amount of PM for the same output power range. Full article

This paper addresses the problem of improving the efficiency of the traction electric drive of an agricultural electric vehicle operating under variable load conditions typical of agricultural transportation. The study substantiates the feasibility of employing a low-power (2 kW) induction motor as a cost-effective, technically robust, and reliable solution for mobile power systems. Particular attention is given to the operating characteristics of the traction drive under fluctuating loading conditions, which significantly affect the energy efficiency and overall performance of agricultural electric vehicles. A comprehensive structural and mathematical model of the induction motor was developed based on a proprietary implementation without the use of standard MATLAB R2020b/Simulink library blocks. The model was formulated using the transformation of a three-phase coordinate system into a two-phase stationary α–β reference frame, enabling a more accurate representation of the electromagnetic and electromechanical processes occurring within the machine. The analysis was carried out with consideration of transient processes, dynamic characteristics, and energy performance under realistic conditions regarding the influence of control strategies on the energy consumption of the electric drive system. The results of this can be applied to the design and optimization of electric transportation systems for agricultural applications, as well as to the development of energy-efficient control algorithms for traction electric motors. Full article

During train operation, railway traction-returning current generates a power-frequency magnetic field around the rail, offering a potential energy source for self-powered trackside monitoring nodes. The H-shaped-core magnetic field energy harvester (MFEH) is attractive because it can be installed beneath the rail without enclosing the conductor, yet its output is strongly affected by the coupled rail-core-coil system. To clarify these effects, a three-dimensional electromagnetic-circuit-coupled finite-element model of an experimentally validated laminated-silicon-steel H-shaped-core MFEH was established to examine core and coil parameters. Increasing the center-leg and side-leg lengths weakens demagnetization but intensifies eddy-current losses, causing output power to approach saturation. Under a 50 Hz, 300 A current in a 54E1 rail and series-tuned matching, output power approaches 5.1 W beyond a center-leg length of 1000 mm and 3.25 W beyond a side-leg length of 700 mm. Within the investigated ranges, center-leg and side-leg lengths of approximately 800 and 400 mm provide the best power–volume performance, respectively. Increasing side-leg height or width also improves output. A larger coil span improves output by reducing internal resistance, whereas more turns yield diminishing gains because of higher winding and eddy-current losses. These findings provide a quantitative basis for parametric design of H-shaped-core MFEHs in railway environments. Full article

In the context of growing attention to environmental sustainability, emission reduction efforts increasingly involve all sectors, including agriculture. European “Stage” regulations (from Stage I in 2002 to Stage V in 2019) have progressively reduced regulated pollutants such as hydrocarbons (HC), nitrogen oxides (NO_x), particulate matter (PM), and carbon monoxide (CO). However, carbon dioxide (CO₂) emissions from agricultural tractors are not currently subject to specific legislation. This study assesses CO₂ emissions through their direct relationship with fuel consumption. Hourly and specific CO₂ emissions (g/kWh) were estimated using power and fuel consumption data from 877 tractors tested under OECD Code 2 procedures from the 1960s to the present. The same tractors were analyzed under two operating conditions: power take-off (PTO) dynamometer bench tests and drawbar tests, considering maximum power and rated engine speed. The four testing conditions were compared to assess differences in delivered power, fuel consumption, and CO₂ emissions. Fuel consumption was modeled through linear regression using power as the independent variable, while specific fuel consumption and fuel productivity were estimated using a nonlinear regression approach. The comparison between test conditions shows a reduction in delivered power of 21.2% when moving from the PTO dynamometer test at maximum power to the drawbar test at rated engine speed, accompanied by an 18.9% increase in specific CO₂ emissions. These findings indicate that operating conditions significantly influence tractor carbon emissions and suggest that assessments accounting for traction-related losses provide a more realistic estimate of tractor environmental performance than PTO dynamometer tests alone. The proposed approach may support the development of carbon-oriented mitigation strategies and future greenhouse gas reduction policies for agricultural mechanization. Full article

The article deals with the further development of national innovative subway cars with asynchronous electric drives and energy recovery systems through the introduction of capacitive energy storage. It has been determined that the assessment of the effectiveness of existing concepts for energy processes control of subway cars with asynchronous electric drives and capacitive energy storage under identical specified conditions remains a relevant issue. Five of the most promising concepts for managing energy processes were selected and idealized. Oscillograms of energy flows for the selected concepts are presented. Parameters for evaluating the effectiveness of the selected control concepts are presented. The scientific novelty lies in the development of a procedure for selecting a rational concept for controlling energy processes in subway rolling stock with asynchronous electric drives and CES, based on the application of a unified comparative analysis system using a comprehensive evaluation criterion. A scheme for replacing subway cars with asynchronous electric drives and capacitive energy storage is presented, and a mathematical model of energy flow processes for traction and regenerative braking modes has been developed based on this scheme. Algorithms for controlling energy processes between asynchronous electric drives, capacitive energy storage devices, and contact networks have been developed for each of the selected concepts. The efficiency of each of the five selected concepts for the same specified operating conditions of the subway cars, parameters of the asynchronous traction electric drive and capacitive energy storage device has been investigated using the developed mathematical model and the formulated comprehensive evaluation criterion. It was established that it is possible to save up to 18% of the electricity consumed from the contact network per braking-acceleration cycle under the specified operating conditions, parameters of the subway cars, asynchronous traction electric drive, and capacitive energy storage device. An additional possibility exists to reduce the installed power of the power supply system equipment by up to 33.5% under the specified operating conditions of a subway train with the proposed technical characteristics. It has been determined that the most rational concept for controlling energy processes in subway cars with asynchronous electric drives and capacitive energy storage is the fifth concept, which allows the use of stored energy from regenerative braking in both normal and emergency operation of the subway power supply system. Full article