Regenerating trains are now in common use on many DC fed railway systems, and train operating companies are able to get a discount on their energy costs if regeneration is active. The electrical energy consumption in a DC system is significant, and a comprehensive understanding of how regeneration affects the overall system energy consumption has not been developed. This study presents a simulation method in which a multi-train analysis is used to determine the system energy consumption with and without regeneration in operation, as well as the impact on the system energy consumption of different headways. The results are used to determine a full ‘energy audit’ of the system based on the data of the Beijing Yizhuang subway line. This includes the energy supplied by the substations, the energy wasted in the power transmission network, the energy used by the train in traction and regenerated by braking trains. The initial results show that regenerating trains have a significantly lower substation demand, but slightly more energy is lost within the network. The results also indicate that, the available regenerative energy and total substation demand vary with different timetables, and there is a 27% difference between the best and worst headways.

Owing to cell-to-cell variations in battery pack, it is hard to manage cells of the battery pack safely and effectively. As a result, the battery pack performance is rapidly degraded, which in turn spread the differences in individual cells. Ambient temperature is a significant factor that influences the characteristics of lithium-ion battery and cell variations in the pack. This study tries to put effort in researching the temperature characteristics of cells series battery pack. The battery model parameters identification tests are designed to analyse the inconsistency characteristics of cells [such as open-circuit voltage (OCV), ohmic and polarisation resistances and polarisation capacitance] under various ambient temperatures. The results indicate that ohmic and polarisation resistances are most significantly increased as the temperature decreases, whereas OCV is opposite. The variation of cells inconsistency characteristics is obvious along with the temperature change. Comprehensive above factors, the change rate of battery pack capacity is faster than that of a single cell with the drop in temperature.

Many railway vehicles use diesel as their energy source but exhaust emissions and concerns about economical fuel supply demand alternatives. Railway electrification is not cost effective for some routes, particularly low-traffic density regional lines. The journey of a regional diesel–electric train is simulated over the British route Birmingham Moor Street to Stratford-upon-Avon and return to establish a benchmark for the conceptual design of a hydrogen-powered and hydrogen-hybrid vehicle. A fuel cell power plant, compressed hydrogen at 350 and 700 bar, and metal-hydride storage are evaluated. All equipment required for the propulsion can be accommodated within the space of the original diesel–electric train, while not compromising passenger-carrying capacity if 700 bar hydrogen tanks are employed. The hydrogen trains are designed to meet the benchmark journey time of 94 min and the operating range of a day without refuelling. An energy consumption reduction of 34% with the hydrogen-powered vehicle and a decrease of 55% with the hydrogen-hybrid train are achieved compared with the original diesel–electric. The well-to-wheel carbon dioxide emissions are lower for the conceptual trains: 55% decrease for the hydrogen-powered and 72% reduction for the hydrogen-hybrid assuming that the hydrogen is produced from natural gas.

Railway Operators and Infrastructure Owners are required to design the railway to specific national and international, technical and safety performance standards. These standards and codes of practice provide the basis for company ‘Codes of Practice’, which detail the design methodology, application and system installation. To validate the design and to comply with these standards and codes of practice, Atkins and the University of Birmingham have developed the multi-train simulator (MTS) to model AC railway electrification infrastructure. The development was carried out under a Knowledge Transfer Partnership between Atkins and the University of Birmingham. The MTS models multiple trains moving on AC traction railway networks following specified timetables. The model of the traction power network covers all types of AC feeding arrangements in the UK, including the rail-return system, the classic booster transformer system and the autotransformer system. This study addresses the work undertaken by the Knowledge Transfer Partnership and describes the development of AC railway electrification infrastructure modelling based on a multi-conductor model for MTS. The modelling of multi-conductors in AC power networks separately, instead of lumping them together, enables more accurate calculations of induced voltage, EMC analysis, return current distribution, positive and negative energy consumptions and loss calculations.

This study proposes a real-time torque-distribution strategy for a pure electric vehicle (EV) with multi-electric propulsion system. This electric propulsion system is composed of three motors – an indirectly driven traction motor for front wheels and two in-wheel motors installed inside both rear wheels. Their torque distribution is determined by particle swarm optimisation (PSO) theory for minimising energy consumption according to the torque-speed-efficiency maps of all the traction motors. The energy consumption over the New European Driving Cycle is analysed for the EV with multiple traction motors. Simulation results by hardware-in-the-loop show that the driving efficiency of the proposed real-time torque-distribution strategy by the PSO is close to the results from the global optimisation method by dynamic programming.

High-frequency resonances occur frequently in traction power supply systems of high-speed railways in China. In this study, the autotransformer-fed traction power supply system for a high-speed railway was first described, and then the high-frequency resonance mechanism was analysed. On the basis of this, the input impedance at the train's pantograph was applied to identify the resonant frequencies, and a second-order high-pass filter was designed by solving the minimum total input impedance with the steepest descent method. The designed filter can not only suppress the existing resonance, but also avoid a new resonance occurrence. Finally, the resonance mechanism analysis and the designed filter are verified by investigating the harmonic spectrum of the pantograph voltage in a MATLAB/Simulink-based co-simulation.

AC–DC converter switches of the drive train of series hybrid electric vehicles (SHEVs) are generally exposed to the possibility of outbreak open-phase faults because of troubles with the switching devices. In this framework, the present study proposes an artificial neural network (ANN)-based method for fault diagnosis after extraction of a new pattern. The new pattern under AC–DC converter failure in view of SHEV application has been used for train-proposed ANN. To achieve this goal, four different levels of switches fault are considered on the basis of both simulation and experimental results. Ensuring the accuracy and generalisation of the introduced pattern, several parameters have been considered, namely: capacitor size changes, load, and speed variations. The experimental results validate the simulation results thoroughly.

As the technology supporting electric vehicles (EVs) is rapidly progressing and the cost of EV components is reducing, EVs are becoming more feasible for use in Australia and in many countries around the world. However, the public perception of EVs and their perceived limitations result in a slow uptake of the technology, partially because of the uncertainty regarding the ability of an EV to meet the driving needs of the general population. Range anxiety is a particular concern with drivers having fear of being stranded by a depleted EV battery. This study explores means of reducing range anxiety by taking into account a variety of environmental and behavioural factors. By considering such factors and implementing it in conjunction with a recently proposed improved state of charge (SoC) estimation method by the authors, a range estimate can be produced that is much more accurate than the conventional methods which consider the SoC and vehicle efficiency alone. This range estimate can be used to inform the driver of the capabilities of the EV and advise if a recharge is required to reach the intended destination.

Double-rotor switched reluctance machine (DRSRM) is a new type of switched reluctance machine that is composed of two rotors and one stator integrated in one machine housing, which is potentially more compact, lower cost, and enables two mechanical outputs suitable for hybrid electric transmissions. This study presents detailed design and simulation procedures of a DRSRM. Various electromagnetic design aspects are investigated to comprehensively analyse the DRSRM design. Loss analysis and thermal simulations are performed to evaluate the thermal loading, and a DRSRM drive model is built to simulate the DRSRM performance. Then a DRSRM prototype is manufactured and tested. The test results confirmed the design and simulations with good agreements.

This study examines the effects of different rotor skew patterns on the cogging torque, the excitation torque ripple, the average torque, and the axial force in an interior permanent magnet synchronous motor. A genetic algorithm is used to minimise the cogging torque for different skew patterns based on analytical functions. The optimal design obtained is verified with finite element analysis. The results show that the linear skew patterns reduce the cogging torque, but increase the axial force. Four- and five-step symmetric skew, herring-bone skew, and five-step W-shaped skew patterns provide an adequate reduction in the cogging torque and axial force, but they have higher excitation torque ripple compared with the linear skew pattern.

Pragmatic approaches are proposed to enhance battery state estimation using Kalman filter (KF) and extended KF. Notable novelties introduced include: the use of state/parameter constraints, asymmetric equivalent circuit model behaviour, inclusion of nominal models, and current sensor measurement bias estimation and compensation. The so-called delta parameters are estimated to handle cell variations, aging, and online deviation of parameters. Strategic simplifications that enable the use of traditional KF algorithm are described. Unique filter structures are presented for state-of-charge and state-of-health estimation, the latter focuses on capacity and impedance estimation. The performance of the proposed approaches is demonstrated on experimental drive-cycle data designed for electric vehicle (EV) and hybrid EV applications.