IET Electrical Systems in Transportation最新文献

In this study, an Integrated Taguchi method-assisted polynomial Metamodelling & Genetic Algorithm (ITM&GA)-based optimisation technique is implemented for design optimisation of a surface inset permanent magnet synchronous motor (SIPMSM). The motor geometry is analysed by implementing the finite element method for application of the motor in electric compressors of the cooling system of an electric vehicle (EV). The polynomial surrogate model is computed with the help of Taguchi experiments to eliminate the redesigning process of models to reach the optimum values of design parameters and reduce the ambiguity to select the best optimum solution in Traditional Taguchi Method. The root-mean-square error test is performed to validate the accuracy of metamodels. The optimum solutions are then converged using the GA technique. The optimum results are compared and presented. Using the ITM&GA technique, the reduction in unwanted ripples in torque and cogging torque along with the improved torque performance of the motor is achieved successfully. The proposed mechanism is effective in obtaining quick and accurate solutions for preliminary designs of the SIPMSM for the electric compressor application in EVs.

The inconvenient nature of non-ideal charging characteristics is demonstrated from a power system point of view. A new adaptive charging algorithm that accounts for non-ideal charging characteristics is introduced. The proposed algorithm increases the local network capacity utilization rate and reduces charging times. The first unique element of the charging algorithm is exploitation of the measured charging currents instead of ideal or predefined values. The second novelty is the introduction of a short-term memory called expected charging currents. This makes the algorithm capable of adapting to the unique charging characteristics of each vehicle individually without the necessity to obtain any information from the vehicle or the user. The proposed algorithm caters to various non-idealities, such as phase unbalances or the offset between the current set point and the real charging current but is still relatively simple and computationally light. The algorithm is compatible with charging standard IEC 61851 and is validated under different test cases with commercial electric vehicles.

Alternating current power supplies and rolling stock with 25 kV (50 or 60 Hz) and 15 kV (16.7 Hz) traction systems do not have the characteristics and behaviour of a typical three-phase medium-voltage distribution system. Switching inductive loads with a vacuum circuit breaker (VCB) in MV traction systems poses familiar challenges as well as some unique challenges, such as the crossing of phase change neutral sections. Transformers represent highly inductive loads due to their iron core and, thus, the consequences of energizing and disconnecting a transformer and dealing with the energy stored in its inductance must be considered within a system context. The authors of this study consider two transient phenomena associated with switching single-phase, medium voltage, AC traction transformer loads using a VCB on railway rolling stock: (i) switching transients that occur when disconnecting a transformer, particularly if lightly loaded and (ii) pre-ignition and current inrush that occurs when energizing a transformer. Both phenomena can cause reliability problems, requiring increased system maintenance or resulting in premature failures of system components. The authors review the use of controlled switching and other state-of-the-art methods to prevent or limit voltage transients when switching a transformer load by means of a VCB. The effective application of such techniques has been demonstrated in previous research or established in practical applications by manufacturers and electrical distribution network companies.

This article presents a coordinated operation model for energy management of a multi-integrated energy system based on Mixed-Integer Linear Programing (MILP). The power derived by trains from regenerative braking energy (RBE), during deceleration, is utilised to meet the interconnected energy hubs’ (IEHs) demand. The recovered energy is calculated by simulating the motion of the trains in MATLAB software. The electricity and heat demand response (DR) programs are integrated into the proposed model to study their impacts on the operating cost and the carbon emission of the IEH, considering several case studies. Furthermore, the uncertainties of the RBE, photovoltaic power generation, and loads of the IEH are considered by formulating the optimisation problem stochastically through a scenario-based approach. Therefore, a scenario generation and reduction decision-making technique is employed. Finally, the GAMS optimisation software is used to assess the efficiency of the presented MILP model. The simulation results indicate that the total operating cost of the IEH reduced 2.0% and 1.4% in the case studies. Also, the CO₂ emission is decreased by about 0.3% by applying the coordination scheme besides the DR programs.

Power Line Communication (PLC) is used to transmit high-fidelity data on internal cell characteristics from within instrumented cells to an external Battery Management System (BMS). Using PLC is beneficial, as it avoids the need for a complex and heavyweight wiring harness within a battery. The use of advanced modulation, such as Quadrature Amplitude Modulation (QAM), is considered here. The existing experimental results of lithium-ion cell impedance characteristics for frequencies of 100 kHz–200 MHz are exploited in order to create a realistic battery model. This model is used to determine the effectiveness and optimal properties of PLC with QAM, as a means of in situ battery communication for Battery Electric Vehicles (BEVs) in combination with a real-world dynamic drive profile. Simulations reveal that the performance of the PLC system is heavily dependent on the selected carrier frequency due to the significant changes in reactance and internal resistance of the lithium-ion cells tested. Furthermore, cells placed in parallel display a decreased performance compared with cells in series. The results highlight that the optimal carrier frequency for in situ QAM-based PLC for a lithium-ion battery system is 30 MHz, and that additional signal conditioning is required for 4-QAM and higher modulation orders.

Electric vehicles (EVs) require an onboard battery charger unit and a battery management system (BMS) unit that balances the voltage levels for each battery cell. So far, both units are two completely autarkic power electronics systems. The circuit presented here operates as a battery charger when the EV is connected to the grid and as a voltage balancer when the EV is driving. Thus, the proposed circuit utilises two functions in one and therefore eliminates the need of having two autarkic units reducing complexity and reduction in component count. The proposed circuit operates as a flyback converter and achieves power factor correction during battery charging. The constant-current constant-voltage (CC–CV) charging method is employed to charge the batteries. However, to limit the number of sensors that will be employed as a result of varying cells during charging, the battery current is estimated using a single current transducer and embedding a converter model in the controller. The operation of the circuit is presented in detail and is supported by simulation results. A laboratory prototype is built to verify the effectiveness of the proposed topology. Experiment results show that the proposed method provides an integrated solution of on-board charging and voltage equalisation.

This study assesses the performance of a multivariate multi-step charging load prediction approach based on the long short-term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15-min resolution for each type of charging site. The second contribution of the work is the extended short-term forecast horizon. A multi-step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4-time step prediction yields considerably superior results compared with the 96-time step forecast.

Most utilities across the world already have demand response (DR) programs in place to incentivise consumers to reduce or shift their electricity consumption from peak periods to off-peak hours usually in response to financial incentives. With the increasing electrification of vehicles, emerging technologies such as vehicle-to-grid (V2G) and vehicle-to-home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, electric vehicles (EV) become distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Here, an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) is proposed. Q-learning, an RL strategy based on a reward mechanism, is used to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue.

The stray current produced by a direct current (DC) transit system leads to the corrosion of rails and metallic structures buried under and around the metro line. One practical approach to diminish current injection into buried structures is to implement stray current collectors underneath the rail. An analytical method is proposed herein using voltage distribution indices (VDIs) to calculate the voltage and current collection level of each collector located under the rail soil, and the solutions are validated by comparing with modelling results. The proposed method helps to achieve the voltage of different points under the ground and the current reaching the collectors using a circuit model, without facing the limitations of practical methods, as a supplementary study for experimental tests. Moreover, the effect of different arrangements of collectors under running rails is modelled. The analytical method is deployed considering two objectives, that is stray current collection by the conductors and a reduction in the installation cost of the conductors. Various arrangements of stray current collectors are compared and evaluated. Using integral and matrix equations, current density and potential at different points under the soil are calculated and the obtained results are compared with European (EN) standards.

Despite low energy and fuel consumption levels in the rail sector, further improvements are being pursued by manufacturers and operators. Their primary efforts aim to reduce traction energy demand, replace diesel, and limit the impact of electrified overhead infrastructures. From a system-level perspective, the integration of alternative energy sources on board rail vehicles has become a popular solution among rolling stock manufacturers. Surveys are made of many recent realizations of multimodal rail vehicles with onboard electrochemical batteries, supercapacitors, and hydrogen fuel cell systems. The ratings, technical features, and operating data of onboard sources are gathered for each application, and a comparison among different technologies is presented. Traction system architectures and energy-control strategies of actual multimodal units are explored and compared with literature research. Moreover, the maturity and potential of recent technologies and alternative topologies of power converters for multimodal traction systems are discussed. Ultimately, onboard storage systems are compared with other solutions for energy-saving and catenary-free operation, with particular focus on their current techno-economic attractiveness as an alternative to diesel propulsion.