Optimizing speed profiles for sustainable train operation with wayside energy storage systems

Large hauling capability and low rolling resistance has put rail transit at the forefront of mass transportation mode sustainability in terms of congestion mitigation and energy conservation. As such, rail vehicles are one of the least energy-intensive modes of transportation and least environmentally polluting. Despite, these positives, improper driving habits and wastage of the braking energy through dissipation in braking resistors result in unnecessary consumption, extra costs to the operator and increased atmospheric greenhouse gas emissions. This study presents an intelligent method for the optimization of the number and locations of wayside energy storage system (WESS) units that maximize the net benefits of the operation of a rail line. First, the optimized speed profiles with and without WESS is determined for a single alignment segment. Then, using the speed profiles obtained as an input, the number and locations of the WESS units that maximize the net benefit is determined for an entire rail line. The energy recovery methods used comprise optimal coasting, regenerative braking, and positioning of the energy storage devices to achieve maximum receptivity. Coasting saves energy by maintaining motion with propulsion disabled, but this increases the total travel time. Regenerative braking converts the kinetic energy of the train into electrical energy for the powering of subsequent acceleration cycles and although it does not affect travel time, it reduces the time available for coasting, indicative of a tradeoff. The study entails the design of a model that simulates the movement of the train over an existing alignment section while considering alignment topography, speed limits, and train schedule. Since on-time performance is the priority of railroad operations, the simulator instructs the driver to operate according to several motion regimes to optimize the energy consumption while maintaining schedule. The model consists of several time-varying inputs which add increased levels of complexity to the problem. This, in addition to its combinatorial nature, necessitates a heuristic algorithm to solve it, because traditional analytical solution methods are deficient. The optimization problem is solved by applying Genetic Algorithms (GA) because of their ability to search for a global solution in a complex multi-dimensional space. This strategy adds sustainability and reduces the carbon footprint of the operator. A case study is conducted on a single segment of a commuter rail line and yields a 34% energy reduction. The case study is extended to an entire line with multiple segments where the aim is to optimize the locations of wayside energy storage devices (WESS) for maximum economic benefit. It was found that out of the 10 alignment segments in the study, a maximized benefit of over $600,000 was achieved with WESS units installed on only three of those segments. The methods derived in this study can be used to generate speed profiles for planning purposes, to assist in recovery from service disruptions, to plan for infrastructural upgrades related to energy harvesting or to assist in the development of Driver Advisory Systems (DAS)

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Data mining approach for range prediction of electric vehicle

Our work proposal is based on the past driving data that are stored in a driver profile, and using real time information about the Electric Vehicle parameters (e.g. speed and energy stored in the batteries), combined with external parameters (e.g. condi-tions of roads, traffic, and weather), determine the range autonomy accurately, taking into account the historical driver behavior. The driver profile is based on the stored data, which acts as training set for a Data Mining approach, in order to estimate the Electric Vehicle range. The Data Mining approach uses a regression model aiming to find the better range autonomy, which is used to represent the current Electric Vehicle range autonomy on a map.Fundação para a Ciência e a Tecnologia (FCT

A comprehensive study of key Electric Vehicle (EV) components, technologies, challenges, impacts, and future direction of development

Abstract: Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers