An Algebraic Approach to DC Railway Electrification Verification

Gröbner bases have been applied to a number of problems related to the verification of Knowledge-Based Systems (KBS) and other problems within graph theory. In particular, the authors have developed in previous papers algebraic approaches to decide whether a situation in a railway interlocking system is safe or not. These algebraic approaches stand out because of the briefness of the code (as they use implementations of well-known algorithms for solving linear or algebraic systems provided by computer algebra systems). The authors have also developed a matrix-based computer tool that helps an expert to check whether a proposed railway electrification scenario (given through the topology of the railway station and the position of the isolation devices and the position and state of the “electrical bypasses” and feeders) fulfills the mandatory requirements of the Spanish railway infrastructure administrator (ADIF) for 3000 V railway electrifications or not. The second author works in the field of railway electrifications and he compares the present-day methods for verification of railway electrifications with the way KBS were verified in the past (manually by experts). In this article we approach this latter problem using algebraic techniques. The new computer tool is based on an algebraic translation of the problem (instead of based on the use of matrices) and is really simple and fast. Determining which electrification sections are under electric tension is computed solving linear systems (because in this case the graph is undirected and no polynomials of degree >1 arise in the algebraic translation, so it is not necessary to use Gröbner bases, unlike in the two problems mentioned in the beginning of the Abstract). Therefore far bigger railway facilities can be addressed than if non-linear systems were involved

Energy storage systems to exploit regenerative braking in DC railway systems: Different approaches to improve efficiency of modern high-speed trains

The growing attention to environmental sustainability of transport systems made necessary to investigate the possibility of energy optimization even in sectors typically characterised by an already high level of sustainability, as in particular the railway system. One of the most promising opportunity is the optimization of the braking energy recovery, which has been already considered in tramway systems, while it is traditionally overlooked for high-speed railway systems. In this research work, the authors have developed two simulation models able to reproduce the behavior of high-speed trains when entering in a railway node, and to analyze the impact of regenerative braking in DC railway systems, including usage of energy storage systems. These models, developed respectively in the Matlab-Simscape environment and in the open source Modelica language, have been experimentally validated considering an Italian high-speed train. After validation, the authors have performed a feasibility analysis considering the use of stationary and on-board storage systems, also by taking into account capital costs of the investment and annual energy saving, to evaluate cost-effectiveness of the different solutions. The analysis has shown the possibility to improve the efficiency of high-speed railway systems, by improving braking energy recovery through the installation of such storage systems

An Event-Based Synchronization Framework for Controller Hardware-in-the-loop Simulation of Electric Railway Power Electronics Systems

The Controller Hardware_in_the_loop (CHIL) simulation is gaining popularity as a cost_effective, efficient, and reliable tool in the design and development process of fast_growing electrified transportation power converters. However, it is challenging to implement the conventional CHIL simulations on the railway power converters with complex topologies and high switching frequencies due to strict real_time constraints. Therefore, this paper proposes an event-based synchronization CHIL (ES_CHIL) framework for high_fidelity simulation of these electrified railway power converters. Different from conventional CHIL simulations synchronized through the time axis, the ES_CHIL framework is synchronized through the event axis. Therefore, it can ease the real_time constraint and broaden the upper bound on the system size and switching frequency. Besides, models and algorithms with higher accuracy, such as the diode model with natural commutation processes, can be used in the ES-CHIL framework. The proposed framework is validated for a 350 kW wireless power transformer system containing 24 fully controlled devices and 36 diodes by comparing it with Simulink and physical experiments. This research improves the fidelity and application range of the power converters CHIL simulation. Thus, it helps to accelerate the prototype design and performance evaluation process for electrified railways and other applications with such complex converters

Model-based comparison of hybrid propulsion systems for railway diesel multiple units

In order to reduce operating costs, railway vehicle operators need to find technical solutions to improve the efficiency of railway diesel multiple units on non-electrified railway routes. This can be achieved by hybridization of diesel multiple unit propulsion systems with electrical energy storage systems to enable brake energy recuperation. After highlighting the state of the art of hybrid railway vehicles and electrical energy storage systems, a simulation model of a generic diesel multiple unit in a 3-car formation is developed and equipped with three types of hybrid power transmissions. Simulations on realistic service profiles with different driving strategies show the potential for fuel consumption reduction for the different transmission types. On a suburban service profile, a 3-car diesel multiple unit is able to achieve simulated fuel savings of up to 24.1% and up to 18.9% on a regional service profile

A review on power electronics technologies for electric mobility

Concerns about greenhouse gas emissions are a key topic addressed by modern societies worldwide. As a contribution to mitigate such effects caused by the transportation sector, the full adoption of electric mobility is increasingly being seen as the main alternative to conventional internal combustion engine (ICE) vehicles, which is supported by positive industry indicators, despite some identified hurdles. For such objective, power electronics technologies play an essential role and can be contextualized in different purposes to support the full adoption of electric mobility, including on-board and off-board battery charging systems, inductive wireless charging systems, unified traction and charging systems, new topologies with innovative operation modes for supporting the electrical power grid, and innovative solutions for electrified railways. Embracing all of these aspects, this paper presents a review on power electronics technologies for electric mobility where some of the main technologies and power electronics topologies are presented and explained. In order to address a broad scope of technologies, this paper covers road vehicles, lightweight vehicles and railway vehicles, among other electric vehicles.This work has been supported by FCT – Fundação para a Ciência e Tecnologia with-in the Project Scope: UID/CEC/00319/2020. This work has been supported by the FCT Project DAIPESEV PTDC/EEI-EEE/30382/2017, and by the FCT Project new ERA4GRIDs PTDC/EEI-EEE/30283/2017. Tiago Sousa is supported by the doctoral scholarship SFRH/BD/134353/2017 granted by FCT

Power Quality in Electrified Transportation Systems

"Power Quality in Electrified Transportation Systems" has covered interesting horizontal topics over diversified transportation technologies, ranging from railways to electric vehicles and ships. Although the attention is chiefly focused on typical railway issues such as harmonics, resonances and reactive power flow compensation, the integration of electric vehicles plays a significant role. The book is completed by some additional significant contributions, focusing on the interpretation of Power Quality phenomena propagation in railways using the fundamentals of electromagnetic theory and on electric ships in the light of the latest standardization efforts

Design, Modelling and Verification of Distributed Electric Drivetrain

The electric drivetrain in a battery electric vehicle (BEVs) consists of an electric machine, an inverter, and a transmission. The drivetrain topology of available BEVs, e.g., Nissan Leaf, is centralized with a single electric drivetrain used to propel the vehicle. However, the drivetrain components can be integrated mechanically, resulting in a more compact solution. Furthermore, multiple drivetrain units can propel the vehicle resulting in a distributed drive architecture, e.g., Tesla Model S. Such drivetrains provide an additional degree of control and topology optimization leading to cheaper and more efficient solutions. To reduce the cost, the drivetrain unit in a distributed drivetrain can be standardized. However, to standardize the drivetrain, the drivetrain needs to be dimensioned such that the performance of a range of different vehicles can be satisfied. This work investigates a method for dimensioning the torque and power of an electric drivetrain that could be standardized across different passenger and light-duty vehicles. A system modeling approach is used to verify the proposed method using drive cycle simulations. The laboratory verification of such drivetrain components using a conventional dyno test bench can be expensive. Therefore, alternative methods such as power-hardware-in-the-loop (PHIL) and mechanical-hardware-in-the-loop (MHIL) are investigated. The PHIL test method for verifying inverters can be inexpensive as it eliminates the need for rotating electric machines. In this method, the inverter is tested using a machine emulator consisting of a voltage source converter and a coupling network, e.g., inductors and transformer. The emulator is controlled so that currents and voltages at the terminals resemble a machine connected to a mechanical load. In this work, a 60-kW machine emulator is designed and experimentally verified. In the MHIL method, the real-time simulation of the system is combined with a dyno test bench. One drivetrain is implemented in the dyno test bench, while the remaining are simulated using a real-time simulator to utilize this method for distributed drivetrain systems. Including the remaining drivetrains in the real-time simulation eliminates the need for a full-scale dyno test bench, providing a less expensive method for laboratory verification. An MHIL test bench for verification of distributed drivetrain control and components is also designed and experimentally verified

Evaluation and optimisation of traction system for hybrid railway vehicles

Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)