SmartDrive: Traction Energy Optimization and Applications in Rail Systems

This paper presents the development of SmartDrive package to achieve the application of energy-efficient driving strategy. The results are from collaboration between Ricardo Rail and the Birmingham Centre for Railway Research and Education (BCRRE). Advanced tram and train trajectory optimization techniques developed by BCRRE as part of the UKTRAM More Energy Efficiency Tram project have been now incorporated in Ricardo's SmartDrive product offering. The train trajectory optimization method, associated driver training and awareness package (SmartDrive) has been developed for use on tram, metro, and some heavy rail systems. A simulator was designed that can simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on a typical AC or DC powered route. The energy evaluation results from the simulator will provide several potential energy-saving solutions for the existing route. An enhanced Brute Force algorithm was developed to achieve the optimization quickly and efficiently. Analysis of the results showed that by implementing an optimal speed trajectory, the energy usage in the network can be significantly reduced. A driver practical training system and the optimized lineside driving control signage, based on the optimized trajectory were developed for testing. This system instructed drivers to maximize coasting in segregated sections of the network and to match optimal speed limits in busier street sections. The field trials and real daily operations in the Edinburgh Tram Line, U.K., have shown that energy savings of 10%-20% are achievable

Predicting the traction power of metropolitan railway lines using different machine learning models

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Rail Transportation on 2021, available online: http://www.tandfonline.com/10.1080/23248378.2020.1829513[EN] Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a task which is traditionally carried out using deterministic models which rely on data measured through money- and time-consuming methods. Using four basic (and cheap to measure) features (train speed, acceleration, track slope and radius of curvature) from MetroValencia (Spain), we predicted the traction power using different machine learning models, obtaining that a random forest model outperforms other approaches in such task. The results show the possibility of using basic features to predict the traction power in a metropolitan railway line, and the chance of using this model as a tool to assess different strategies in order to increase the energy efficiency in these lines.This work was supported by the Ministerio de Economia y Competitividad [TRA2011-26602].Pineda-Jaramillo, J.; Martínez Fernández, P.; Villalba Sanchis, I.; Salvador Zuriaga, P.; Insa Franco, R. (2021). Predicting the traction power of metropolitan railway lines using different machine learning models. International Journal of Rail Transportation. 9(5):461-478. https://doi.org/10.1080/23248378.2020.1829513S4614789

A review of modelling and optimisation methods applied to railways energy consumption

[EN] Railways are a rather efficient transport mean, and yet there is increasing interest in reducing their energy consumption and making them more sustainable in the current context of climate change. Many studies try to model, analyse and optimise the energy consumed by railways, and there is a wide diversity of methods, techniques and approaches regarding how to formulate and solve this problem. This paper aims to provide insight into this topic by reviewing up to 52 papers related to railways energy consumption. Two main areas are analysed: modelling techniques used to simulate train(s) movement and energy consumption, and optimisation methods used to achieve more efficient train circulations in railway networks. The most used methods in each case are briefly described and the main trends found are analysed. Furthermore, a statistical study has been carried out to recognise relationships between methods and optimisation variables. It was found that deterministic models based on the Davis equation are by far (85% of the papers reviewed) the most common in terms of modelling. As for optimisation, meta-heuristic methods are the preferred choice (57.8%), particularly Genetic Algorithms.Martínez Fernández, P.; Villalba Sanchis, I.; Yepes, V.; Insa Franco, R. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production. 222:153-162. https://doi.org/10.1016/j.jclepro.2019.03.037S15316222

Multi-agent Near Real-Time Simulation of Light Train Network Energy Sustainability Analysis

As an attractive transportation mode, rail transit consumes a lot of energy while transporting a large number of passengers annually. Most energy-aimed research in rail transit focuses on optimizing the train timetable and speed trajectory offline. However, some disturbances during travel will cause the train to fail to follow the offline optimized control strategy, thus invalids the offline optimization. In the typical rail transit control framework, the moving authority of trains is calculated by the zone controller based on the moving/fixed block system in the zone. The zone controller is used to ensure safety when the travel plan of trains changes due to disturbance. Safety is guaranteed during the process, but the change of travel plan leads to extra energy costs. The energy-aimed optimization problem in rail transit requires ensuring safety, pursuing punctuality with considering track slope, travel comfort, energy transferring efficiency, and speed limit, etc. The complex constraints lead to high computational pressure. Therefore, it is difficult for the regional controller to re-optimize the travel plan for all affected trains in near real-time. Multi-agent systems are widely used in many other fields, which show decent performance in solving complex problems by coordinating multiple agents. This study proposes a multi-agent system with multiple optimization algorithms to realize energy-aimed re-optimization in rail transit under different disturbances. The system includes three types of agents, train agents, station agents and central agents. Each agent exchanges information by following the time trigger mechanism (periodically) and the event trigger mechanism (occasionally). Trigger mechanism ensures that affected agents receive necessary information when interference occurs, and their embedded algorithms can achieve necessary optimization. Four types of cases 5 / 128 are tested, and each case has plenty of scenarios. The tested results show that the proposed system provides encouraging performance on energy savings and computational speed

Estudio y optimización del consumo energético del ferrocarril mediante redes neuronales y algoritmos heurísticos

[ES] El transporte es una actividad fundamental en las sociedades modernas, pero también es uno de los sectores que más contribuye al consumo energético y a las emisiones. El ferrocarril es uno de los medios de transporte más eficientes, pero todavía existe un margen de mejorar para reducir su consumo energético y aumentar su sostenibilidad. Uno de los modos más efectivos para lograr esto, especialmente en ferrocarriles de ámbito urbano, es mediante la optimización de marchas. Existen muchos estudios que afrontan este problema a través de la simulación y el uso de algoritmos que identifican perfiles de conducción eficiente bajo múltiples escenarios. En este contexto, el propósito de la tesis doctoral es contribuir al conocimiento de las herramientas empleadas para estudiar el consumo energético en el ferrocarril de ámbito urbano, centrándose por un lado en estudiar el posible uso de redes neuronales como instrumento de simulación, y por otro lado llevando a cabo una comparativa sistemática de algunos de los algoritmos meta-heurísticos de optimización más usados. Para ello, se ha desarrollado un simulador de marchas combinado con una red neuronal, el cual parte de una serie de comandos que representan la salida del sistema ATO, y calcula el correspondiente perfil de velocidad, tiempo de viaje y consumo energético. El simulador ha sido validado con datos reales medidos en la red de metro de Valencia, con un error medio del 2,87% en la estimación de tiempo y del 3,62% en la estimación de la energía. Mediante este simulador se han calculado más de 5.800 combinaciones de comandos ATO para 32 tramos de la red de metro de Valencia (considerando ambos sentidos de circulación) y se han calculado los frentes de Pareto reales para cada caso. A partir de aquí, se han aplicado cinco algoritmos meta-heurísticos (NSGA-II, MOPSO, SPEA-II, MOEA-D y MOACOr) para obtener conjuntos de soluciones no-dominadas en cada uno de los 64 casos de estudio, y se ha evaluado el grado de convergencia, regularidad y diversidad de cada conjunto de soluciones con una serie de métricas, aplicando un análisis estadístico para detectar diferencias significativas. Se ha observado que los cinco algoritmos ofrecen resultados similares en términos de convergencia y regularidad, pero que el algoritmo MOPSO destaca significativamente en términos de diversidad (y, en menor medida, el algoritmo SPEA-II), siendo el algoritmo más habitual (NSGA-II) el que peor resultados presenta en este sentido. Estos resultados pretenden ofrecer una cierta guía para escoger algoritmos más efectivos en futuros estudios de optimización de marchas ferroviarias.[CA] El transport és una activitat fonamental en les societats modernes, però també és un dels sectors que més contribueix al consum energètic i a les emissions. El ferrocarril és un dels mitjans de transport més eficients, però encara hi ha marge per a reduir el seu consum energètic i augmentar la seua sostenibilitat. Un dels modes més efectius per a fer això, especialment en ferrocarrils d'àmbit urbà, es mitjançant l'optimització de marxes. N'hi ha molts estudis que afronten aquest problema mitjançant la simulació i l'ús d'algoritmes que identifiquen perfils de conducció eficient baix múltiples escenaris. En aquest context, el propòsit d'aquesta tesi doctoral és contribuir al coneixement de les eines utilitzades per a estudiar el consum energètic en el ferrocarril d'àmbit urbà, centrant-se, per una banda, en estudiar l'ús de xarxes neuronals com a instrument de simulació, i per altra fent una comparació sistemàtica d'alguns dels algoritmes meta-heurístics més utilitzats. Per a fer això, s'ha desenvolupat un simulador de marxes combinat amb una xarxa neuronal que, partint d'uns comandaments que representen l'eixida del sistema ATO, calcula el corresponent perfil de velocitats, temps de viatge i consum energètic. El simulador ha estat validat amb dades reals mesurades en la xarxa de metro de València, amb un error mitjà del 2,8% en l'estimació del temps i del 3,62% en l'estimació de l'energia. Mitjançant aquest simulador s'han calculat més de 5.800 combinacions de comandaments ATO per a 32 trams de la xarxa de metro de València (considerant ambdós sentits de circulació), i s'han calculat els fronts de Pareto reials per a cada cas. A partir d'ací, s'han aplicat cinc algoritmes meta-heurístics (NSGA-II, MOPSO, SPEA-II, MOEA-D i MOACOr) per a obtindre conjunts de solucions no-dominades en cadascú dels 64 casos d'estudi, i s'ha avaluat el grau de convergència, regularitat i diversitat de cada conjunt de solucions amb una sèrie de mètriques, aplicant un anàlisi estadístic per a detectar diferències significatives. S'ha observat que els cinc algoritmes ofereixen resultats similars pel que fa a convergència i regularitat, però que l'algoritme MOPSO destaca significativament pel que fa a diversitat (i, en menor grau, l'algoritme SPEA-II), mentre que l'algoritme més habitual (NSGA-II) és el que dona pitjor resultats en aquest sentit. Aquestos resultats pretenen oferir una mena de guia per a escollir algoritmes més efectius en futurs estudis d'optimització de marxes ferroviàries.[EN] Transport is one of the most fundamental activities of modern societies, but it is also one of the sectors with higher energy consumption and associated emissions. Railways are one of the most efficient transport means, but there is still margin to reduce their energy consumption and to improve their sustainability. One of the most effective ways to do so, particularly in urban railways, is to optimise speed profiles. Several studies focus on this problem by using simulation and algorithms to identify efficient speed profiles under different scenarios. In this context, the main objective of this doctoral thesis is to contribute to the knowledge regarding tools used to study railways energy consumption in urban railways. The thesis focuses, on the one hand, on studying the potential use of neural networks as simulation tools. On the other hand, the thesis carries out a systematic comparison between a few of the most used meta-heuristic optimisation algorithms. Hence, a driving simulator combined with a neural network has been developed. This simulator takes a set of commands, which represent the output of the ATO system, and calculates the corresponding speed profile, travel time and energy consumed. The simulator has been validated using real data measured in the Valencia metro network, with an average error of 2.87% (time estimation) and 3.62% (energy estimation). Using this simulator, more than 5,800 combinations of ATO commands have been simulated in 32 inter-station stretches selected from the Valencia metro network (considering both directions of travel) and the corresponding real Pareto fronts have been calculated. Afterwards, five meta-heuristic algorithms (NSGA-II, MOPSO, SPEA-II, MOEA-D and MOACOr) have been used to obtain sets of non-dominated solutions for each of the 64 case studies. The convergence, regularity and diversity of these solution sets have been then evaluated through a series of metrics, applying statistical analysis to determine whether any difference found was significant. The results show that all five algorithms perform similarly in terms of convergence and regularity, but that MOPSO performs significantly better in terms of diversity (and, to a lesser extent, SPEA-II), while the most common algorithm (NSGA-II) yields poorer results. These conclusions aim to offer a guide to choose more effective algorithms for future railways optimisation studies.Martínez Fernández, P. (2019). Estudio y optimización del consumo energético del ferrocarril mediante redes neuronales y algoritmos heurísticos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/134018TESI