IET Electrical Systems in Transportation最新文献

Reversible substations (RSs) permitting bidirectional power flows can recover the regenerative braking energy of trains in DC traction power supply systems (TPSSs), increasing the energy efficiency of railway systems. To predict their effects on system dynamics and energy savings, the paper develops multiresolution models (MRMs) to simulate the RS roles with different fidelities. A high-resolution model for the transient simulation replicates a particular topology where a three-level voltage source inverter is connected to the secondary winding of an existing 12-pulse rectifier transformer and regulated to keep a constant DC voltage in the inverting mode. Furthermore, it can model the transient effects of pantograph-to-line arcing by inserting arc voltage profiles at the train’s input stage. To increase the computation speed in the long-term energy flow simulation, a low-resolution model simplifies the rectifiers into a series connection of a diode and a controlled voltage source depicting their nonlinear output characteristics and then places a DC voltage source in parallel to form a reverse path for braking power recovery. In addition, nonlinear conversion efficiencies are introduced to calculate energy flows across substations. The MRMs are tested based on a 1.5 kV DC TPSS and discussed alongside system dynamics under normal operation or pantograph arcing and the consistencies between different models. The RS using bidirectional voltage source converters only is additionally modelled to compare the technical performance of the two topologies in terms of system dynamics and energy efficiencies.

Rail breaks, which are crucial maintenance issues for the railways, require immediate inspection and maintenance as it can cause severe railway accidents. Though, it is still difficult to promptly detect rail breaks that occur during train operation even with the advances in maintenance and inspection technologies. In this research, as a preliminary study on rail break detection system, a deep learning-based discontinuous rail position classification method, which is using vibration data obtained from distributed acoustic sensing (DAS) system during train operation, is proposed. To analyze the vibration data, a preprocessing algorithm for determining train occupancy is applied first. After that, the data in the space–time domain occupied by the train is converted to the spectrogram which is in the frequency domain by using short-time Fourier transformation (STFT). In the third step, the spectrogram images are applied to the proposed 2D convolutional neural network (2D CNN) model and the network detects discontinuous rail positions along the track, which are geometrically distinct from continuous welded rails, such as rail breaks. In order to evaluate the superiority of the proposed network model, performance comparison tests with other existing models were conducted with data collected from an actual railway line. From the results, the proposed model could achieve 99.17%, 93.33%, 87.5%, 90.32% for accuracy, precision, recall, and F1 score, respectively, and the results show overwhelming detection performance compared to other models.

The high-power direct drive permanent magnet synchronous motor (DD-PMSM) has become a hotspot because of its excellent performance compared to the asynchronous motor. Due to limitations like suspension structure and field clearance, the bottom of the windings is closer to the rail surface in a direct drive system under the same power load, which causes more disturbance energy coupling into the track circuit equipment in the form of magnetic field radiation than that of the asynchronous motor. Therefore, it may cause a wrong decision on track occupancy if the disturbance falls into the sensitive frequency band, leading to signaling failure. This paper first studied the magnetic field radiation from DD-PMSM and its influence on adjacent devices and determined the disturbance coupling path between the motor and the track circuit. Next, the calculation and analysis are accomplished on the radiated magnetic field and electromotive force disturbance. Then the FEM model is established for simulation and verification. Finally, engineering verification is carried out by the field test data to prove the validity of the theoretical model. This research helps to improve the EMC of locomotive and signaling systems, as well as mitigate signaling faults and safety risks.

Permanent magnet synchronous machines (PMSMs) are still the first choice for use in electric vehicles, due to their unparalleled efficiency and power density. However, they suffer from an inherently limited speed range. As field weakening or the addition of a mechanical gearbox deteriorates the efficiency of the drive, it is suggested in this paper to equip the drive with reconfiguration switches, giving rise to a so-called e-gear. The switches—which are implemented by means of mechanical relays—allow to change the winding connection of the electric machine from a series to a parallel connection and hence to double its efficient speed range. Simulations and experimental results on a 4-kW axial-flux PMSM confirm the feasibility of the concept and prove that the reconfiguration can be conducted in less than 35 ms.

Rail potential (RP) has become a disaster for the safe operation of metro lines. In urban rail transit (URT), regional insulation degradation often occurs due to the harsh environment of the reflux conductor for traction current, leading to the complex distribution of RP. In this paper, the dynamic distribution of RP with regional insulation alteration in URT is studied. First, the distribution model of the reflux system with regional insulation alteration is established. Second, utilizing the distributed parameter element method and taking into account the constraint conditions of the concentrated parameters, a superposition calculation method of RP in the reflux system is proposed. Finally, using Guangzhou Metro as an example, the simulation of different insulation states in long and local areas of URT is carried out. Results show that the RP amplitude decreases while the stray current amplitude increases when the insulation is degraded. At the same time, when the insulation in the local area degrades, the RP at the far end increases.

Hybrid electric vehicles (HEVs) require the design of an energy management strategy (EMS). Many EMS are solved using different types of deterministic rules (rule-based (RB)) or optimization-based (OB) methods. The disadvantage of these strategies is that the primary energy flows in the drive are only solved “ex post,” when in principle, they cannot bring a substantial increase in energy recovery. A little-studied HEV traction drive topology is an internal combustion engine (ICE), supercapacitor (SC), traction motor (TM), and electric power divider (EPS) assembly. The original EMS method implemented in this assembly is based on the control of energy flows at the physical level in the DC link node. Changes in the power of the TM, under the condition of zero summation of currents in the DC link, will spontaneously induce energy spillover from and to the supercapacitor. The state of energy (SOE) in the supercapacitor can then be maintained by the balanced power of the ICE. This makes it possible to achieve a reduction in accelerations of approximately 30%. In principle, the presented EMS makes it possible to absorb all the kinetic and potential energy of negative driving resistances, thereby significantly reducing vehicle consumption. The strategy does not even require knowledge of the driving profile and bypasses complicated optimization algorithms. When validating the EMS method on an experimental test bench by implementing the driving cycle into real prototype components of the HEV physical model, a recovery rate of 14% was achieved, but the potential is up to twice that.

The optimization of the parameters of the components related to the radio frequency (RF) transmission circuit of the balise can keep the balise working normally under low power consumption and increase the reliability and stability of the high-speed railway vehicle-ground communication. However, the circuit has high complexity, many parameters need to be considered in optimization, and the constraint relationship is complex. Optimizing a single objective is very difficult and time-consuming. Therefore, this paper proposes a ground transponder design and optimization method based on deep learning. Firstly, the functional modules of the balise RF circuit are decomposed, and the influencing factors of circuit start-up conditions and load quality factors are analysed, and the component parameters that need to be optimized are extracted as decision variables. The objective function of the model is established from the perspective of circuit cost and static power consumption, and a multi-objective optimization model is established through its overall circuit scheme. Finally, in order to reduce the time cost, the multi-objective optimization model is processed by the fusion of neural network and genetic algorithm. Among them, the experimental results show that the optimization effect of Bayesian neural network (BNN) is the most significant, and the static power consumption and cost of the circuit can be reduced by 55% and 42%, respectively, with less time overhead.

This paper addresses challenges related to the short service life and low efficiency of hybrid energy storage systems. A semiactive hybrid energy storage system with an ultracapacitor and a direct current (DC) bus directly connected in parallel is constructed first, and then related models are established for the lithium-ion battery, system loss, and DC bus. Based on the multiobjective evaluation function, a hybrid energy storage system Model Predictive Control-Differential Evolution (MPC-DE) energy management method is proposed. Experiments were conducted under China Light-Duty Vehicle Test Cycle-Passenger Car (CLTC-P) and Highway Fuel Economy Test (HWFET) driving cycles. The experimental results show that compared with other methods, this strategy can effectively reduce the loss of lithium-ion battery and ultracapacitor hybrid energy storage system and improve the system efficiency.

Wheel flat can seriously affect the wheel–rail contact performance and cause impact vibrations, which significantly threaten the safe operation of railway vehicles. It is crucial to accurately assess the effects of wheel flats on the wheel–rail contact performance and the vibration impact on the traction drive system. However, studies related to wheel flats have focused on the mechanical part and have yet to fully consider the electrical and mechanical parts of the entire traction drive system. Therefore, this paper first builds an electromechanical coupling model based on the electric traction drive system and the locomotive-track coupling dynamics model. Then, based on this model, the impact of the wheel flat on the electrical and mechanical parts of the traction drive system under different flat lengths, different flat depths, and different vehicle speeds was analyzed. The results indicate that with the increasing depth of the flat spot, the wheel–rail dynamic response, motor rotor speed, and motor torque exhibit more significant fluctuations. Additionally, as the locomotive speed increases, the impact of the flat on the wheel–rail contact performance intensifies. Wheel flats can excite the 1st bending mode of the wheelset, resulting in vibration shocks at 90 Hz.

This article proposes a dual independent rotor axial flux induction motor (DIR-AFIM) with two degrees of freedom as a propulsion motor for an electric vehicle (EV). The performance of this motor in different operating conditions of the vehicle is discussed and investigated. This motor has two rotors that are mechanically independent of each other, providing driving force for the EV separately and enabling the removal of any mechanical or electrical differential. The propulsion motor can play the role of differential and also reduce the cost and complexity of the entire propulsion system in some extent. The finite element modeling of the proposed motor has been performed and the performance characteristics have been evaluated in three operating scenarios: flat path, sloped path, and turning path, taking into account the dynamics of the vehicle. Additionally, the accuracy of the simulations and modeling has been confirmed by performing some practical tests on the prototype machine. The results show that the simulations and measurements are in good agreement and the proposed propulsion system can be a suitable option for lightweight electric vehicles.