IET Electrical Systems in Transportation最新文献

The move towards More-Electric Aircraft (MEA) and Engine (MEE) systems has resulted in system integrators exploring the use of Direct Current (DC) for primary power distribution to both reduce energy conversion stages and enable parallelling of non-synchronised engine off-take generation. A prevalent challenge of utilising DC systems is the safe management of arc faults. Arcing imposes a significant threat to aircraft and can result in critical system damage. Series intermittent arc faults in DC systems are particularly challenging to detect due to the lack of a zero current crossing coupled with the intermittency caused by in-flight vibrations. Additionally, several state-of-the-art arc fault detection methodologies fail to address the handling of aircraft system-specific conditions such as dynamic loading and stability requirements. This paper addresses these unique MEA/MEE issues through the proposal of a new generalised real-time arc fault detection methodology (WaSp) based on time–frequency domain-extracted features applied to a feed-forward Artificial Neural Network (ANN). The paper outlines the analysis of arc fault time–frequency domain features. Simulation-based case studies emulating a range of on-board EPS conditions are presented and show the proposed system has the potential for highly accurate and generalised detection performance with fast detection times.

With the railway advancement of speed and load capacity, new high-power diesel locomotives occupy a prominent position. Due to the susceptibility of onboard signal equipment comprised of microelectronic devices, the diesel locomotive's traction motor (TM) may produce serious disturbance to cab signalling, possibly distorting the signal pattern via receiving coils, even resulting in mutation and decoding error of cab signals. However, the existing research studies pay little attention to this electromagnetic interference problem. This paper studies the radiated alternating magnetic field of the TM and interference coupling path to onboard equipment. It first analyses the cab signalling working principle and clarifies the interference mechanism, focussing on the quantitative analysis of the radiated magnetic field and interference electromotive force. Then, based on the ANSYS platform, the finite element method is used to complete modelling, simulation, and verification. Finally, combined with the railway field data, a typical interference case is analysed, verifying that the distorted signal pulses under the radiated magnetic field are the primary cause of decoding failures. This research establishes the quantitative relationship between the traction radiation intensity and the interference amplitude on signals, helping explore the countermeasures, improve the cab signal decoding accuracy, and ensure transportation efficiency.

The battery management system (BMS) in electric vehicles monitors the state of charge (SOC) and state of health (SOH) of lithium-ion battery by controlling transient parameters such as voltage, current, and temperature prevents the battery from operating outside the optimal operating range. The main feature of the battery management system is the correct estimation of the SOC in the broad range of vehicle navigation. In this paper, to estimate real-time of SOC in lithium-ion batteries and overcome faults of Extended Kalman Filter (EKF), the Square-Root Sigma Point Kalman Filter is applied on the basis of numerical approximations rather than analytical methods of EKF. For this purpose, the Hybrid Pulse Power Characterisation tests are combined with the non-linear least square method that acquired the second-order equivalent circuit model parameters. Then, the newly developed method is tested with an 18,650 cylindrical lithium-ion battery with a nominal capacity of 2600 mAh in four different ambient temperatures. Finally, the accuracy and effectiveness of the two proposed methods are verified by comparing with results of pulse discharge and dynamic driving cycle tests. The comparison results indicate the error of the proposed algorithm is about 0.02 under the most test conditions.

Traction control is one of the most important functions in vehicle drivetrain systems. When a vehicle is driven on a low-friction road surface, loss of traction force can cause the driven wheels to spin. This reduces vehicle acceleration performance and can even cause the driver to lose control of the vehicle. The high bandwidth of electric machine control in electric vehicles gives more possibilities to regulate driving torque on wheels and prevent wheel spin. An acceleration-based wheel slip control is designed and investigated. Compared to traditional slip-based traction control, the proposed method does not depend on the estimation of the vehicle speed and only relies on the driven wheel rotational acceleration. The control method is verified using the simulation of an electric vehicle with a decentralised electric drivetrain system. The vehicle and the electric drive are modelled in CarMaker and PLECS, respectively. The simulation results show that the proposed method is able to prevent the driven wheel from spinning when the vehicle is accelerated on an ice road. In addition, the control is fast enough and requires only half a second to reduce the wheel acceleration to a normal range.

The electric spark caused by pantograph-catenary (PC) electrical contact failure happens frequently, it is a kind of arcing phenomenon which can severely affect the current collection of PC. The theoretical analysis and modelling of PC electric spark are tried. First, the contact spots between PC and their temperature rise are analysed and derived based on the classical electrical contact theory, respectively. The mechanism of spark discharge is revealed through the analysis of PC voltage difference, and the spark discharge model is proposed combining with the spot temperature rise formula and spark discharge mechanism. Then, in order to obtain some key parameters in an electric spark discharge model, such as contact force and contact current, the structure model of PC and the circuit model of vehicle-grid are established for the calculation of the spark rate in PC. Finally, the spark rate is calculated and analysed under the different parameters in PC system. The calculation results are verified by the experiment results. Through the presentation of the correlation of electric spark discharge and factors, such as contact force, contact current and material of PC, this study can support the assessment of electrical contact quality in the PC system in future study.

Electrification of transportation is one of the new ways to deal with environmental issues. In order to reach sustainable development, efficient integration of the plug-in electric vehicles (PEVs) into the distribution network (DN) plays a significant role. Establishing PEV intelligent parking lots (IPLs) is one of the important solutions to develop charging stations. However, one of the problems with IPLs is restrictions created by municipalities and DNs while the common parking lots are not faced with these constraints. The aim of this paper is to optimally allocate IPLs in order to minimize system costs through a two-stage mathematical model. The system cost involves installation cost, cost of exchanging the electrical power with an upstream grid, cost of the electrical power loss, network reliability cost, and emissions cost. In the first stage, the behaviour of IPLs is optimized taking into account the market interactions for enhancing the profit of the IPL owner. In the second stage, the optimal IPL allocation is performed taking various network constraints into consideration. The robust optimization is developed to hedge the DN operator and the IPL owner against risk imposed uncertain variables. The proposed model is a Robust Mix Integer Linear Problem, which is solved using the CPLEX solver in GAMS software. The effectiveness and the efficiency of the proposed model are evaluated on the IEEE 12-bus test network.

The railway traction loads produce massive harmonics that are injected directly into the traction power system. This causes serious waveform distortion in the supply voltage and even invokes a harmonic parallel resonance, which may lead to significant harmonic current and voltage amplification. Consequently, severe electrical equipment failures and feeder tripping problems occurred frequently in the traction substation. A clear harmonic parallel resonance at a 27.5-kV feeder can be observed from the field measurement result of the traction substation without filter banks. This paper proposes a high-performance single-tuned 3rd passive filter for harmonic mitigation and harmonic resonance suppression in the traction power system. In the ST filter design process, the impact of the quality factor (Q_f) design of the filter reactor on the filtering performance is considered. Furthermore, the driving point impedance as seen from the traction load is also studied. The design and the engineering consideration of the harmonic filter are also presented, which comply with the recommendations of the IEEE and IEC standards. Besides, the unbalance-current protection of a capacitor bank is introduced in this paper. Finally, the proposed filter is implemented in an actual traction substation, from the field measurement results; the proposed harmonic filter delivers very good performance.

Ship motions affect the propulsion system, which causes fluctuations in the power system. Mutually, the power system variations impact the ship velocity by generating speed changes in the propeller. Therefore, interconnecting the ship hydrodynamic and power system has paramount importance in designing and analysing an all-electric ship (AES). The lack of an integrated model that can be evaluated in various operating conditions, such as manoeuvring, is evident. This paper explores the required perceptions for the power system and hydrodynamic analysis of an AES. Then, an integrated theoretical model comprising both the ship motion and power system is proposed. In addition to providing an accurate model for the ship in varying situations, this study demonstrates that the ship speed estimation during a ship route change differs from when the interconnections are overlooked. In the light of this determination, a straightforward enhancement for the ship speed control system is proposed. The effects of this modification on the ship power system are explored using the proposed model. The developed model is examined in different scenarios, and its advantages are discussed. It is shown that this model is suitable for employing in the model-based design of AESs.

Due to the growing emergence of vehicle electrification, agricultural tractor developers are launching hybrid powertrains in which energy management strategy (EMS) assumes a prominent role. This work mainly aims at developing an EMS for a plug-in hybrid electric tractor (PHET) to minimise fuel consumption and increase the operating range. The developed off-road PHET power sources are composed of a biogas-fuelled Internal Combustion Engine Generator (Bio-Gen), a photovoltaic system, and a battery pack. To control the power flow among different sources, a two-layer EMS is formulated. In this regard, initially, the farm operating mode is recognised by means of classification of a working cycle's features. Then, a control strategy based on a multi-mode fuzzy logic controller (MFLC) is employed to manage the power flow. At each sequence, the classifier identifies the farm operation condition and accordingly activates the relative mode of the MFLC to meet the requested power from the Bio-Gen. The performance of the proposed EMS has been evaluated based on three real-world typical agricultural working cycles. The results demonstrate the successful performance of the proposed intelligent EMS under farm conditions by maintaining the energy sources' operation in a high-efficiency zone which can lead to the extension of the working range and decrease fuel consumption.

In this work, a novel three-phase unidirectional off-board Electric Vehicles (EVs) fast charger is proposed. Each phase of the proposed converter consists of a two-stage converter. The first stage is an AC–DC front-end converter with power factor correction (PFC) control. The front-end AC–DC converter is based on an isolated boost converter, which has a boosting capability and provides galvanic isolation. In this stage, parallel-in parallel-out isolated boost converter modules are employed in each phase. This enables sharing the current among modules to avoid implementing one module with a high current rating to meet the fast-charging requirements. The multimodule option provides fault-ride through capability for the proposed charger. The DC outputs of involved phases, that is, three isolated DC voltages, are fed to three cascaded DC–DC unidirectional buck converters. The charging terminals are used to charge the EV battery through a filter inductor with a charging current controller's aid. A detailed illustration of the suggested closed-loop controllers is presented to ensure a successful operation for the proposed architecture. A simulation case study for a 25-kW charger is presented. Finally, a 1-kW prototype is implemented for experimental validation.