Assessment of the worthwhileness of efficient driving in railway systems with high-receptivity power supplies

Eco-driving is one of the most important strategies for significantly reducing the energy consumption of railways with low investments. It consists of designing a way of driving a train to fulfil a target running time, consuming the minimum amount of energy. Most eco-driving energy savings come from the substitution of some braking periods with coasting periods. Nowadays, modern trains can use regenerative braking to recover the kinetic energy during deceleration phases. Therefore, if the receptivity of the railway system to regenerate energy is high, a question arises: is it worth designing eco-driving speed profiles? This paper assesses the energy benefits that eco-driving can provide in different scenarios to answer this question. Eco-driving is obtained by means of a multi-objective particle swarm optimization algorithm, combined with a detailed train simulator, to obtain realistic results. Eco-driving speed profiles are compared with a standard driving that performs the same running time. Real data from Spanish high-speed lines have been used to analyze the results in two case studies. Stretches fed by 1 × 25 kV and 2 × 25 kV AC power supply systems have been considered, as they present high receptivity to regenerate energy. Furthermore, the variations of the two most important factors that affect the regenerative energy usage have been studied: train motors efficiency ratio and catenary resistance. Results indicate that the greater the catenary resistance, the more advantageous eco-driving is. Similarly, the lower the motor efficiency, the greater the energy savings provided by efficient driving. Despite the differences observed in energy savings, the main conclusion is that eco-driving always provides significant energy savings, even in the case of the most receptive power supply network. Therefore, this paper has demonstrated that efforts in improving regenerated energy usage must not neglect the role of eco-driving in railway efficiency

DETECTION PROCESS OF ENERGY LOSS IN ELECTRIC RAILWAY VEHICLES

The paper deals with the detection process of energy loss in electric railway hauling vehicles. The importance of efficient energy use in railways and cost-effective rail transport tendency toward regenerative braking energy are considered. In addition, the current situation and improvement opportunities to achieve efficient energy use are examined. Seven measurement series were performed with scheduled Railjet trains between Hegyeshalom and Győr railway stations in Hungary. This railway section is related to the Hungarian State Railways' No. 1 main railway line (between Budapest-Kelenföld and Hegyeshalom state board), which is a part of the international railway line between Budapest and Vienna (capitals of Hungary and Austria, respectively). This double-track, electrified railway line with traditional ballasted superstructures and continuously welded rail tracks is important due to the international passenger and freight transport between Germany, Austria, and Hungary. The value of the regenerative braking energy can be even 20-30% of the total consumed energy. This quite enormous untapped energy can be used for several aims, e.g., for comfort energy demand (air conditioning, heating-cooling, lighting, etc.) or energy-intensive starts. The article also investigates the optimization of regenerative braking energy by seeking the energy-waste locations and the reasons for the significant consumption. The train operator's driving style and habit have been identified as one of the main reasons. Furthermore, train driver assistance systems are recommended to save energy, which is planned for future research

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)

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

A New Multi-objective Solution Approach Using ModeFRONTIER and OpenTrack for Energy-Efficient Train Timetabling Problem

Trains move along the railway infrastructure according to specific timetables. The timetables are based on the running time calculation and they are usually calculated without considering explicitly energy consumption. Since green transportation is becoming more and more important from environmental perspectives, energy consumption minimization could be considered also in timetable calculation. In particular, the Energy-Efficient Train Timetabling Problem (EETTP) consists in the energy-efficient timetable calculation considering the trade-off between energy efficiency and running times. In this work, a solution approach to solve a multi-objective EETTP is described in which the two objectives are the minimization of both energy consumption and the total travel time. The approach finds the schedules to guarantee that the train speed profiles minimize the objectives. It is based on modeFRONTIER and OpenTrack that are integrated by using the OpenTrack Application Programming Interface in a modeFRONTIER workflow. In particular, the optimization is made by modeFRONTIER, while the calculation of the train speed profiles, energy consumption and total travel time is made by OpenTrack. The approach is used with Multi-objective Genetic Algorithm-II and the Non-dominating Sorting Genetic-II, which are two genetic algorithms available in modeFRONTIER. The solution approach is tested on a case study that represents a real situation of metro line in Turkey. For both algorithms, a Pareto Front of solution which are a good trade-off between the objectives are reported. The results show significant reduction of both energy consumption and total travel time with respect to the existing timetable

Flywheel Energy Storage Systems for Rail

In current non-electrified rail systems there is a significant loss of energy during vehicle braking. The aim of this research has been to investigate the potential benefits of introducing onboard regenerative braking systems to rail vehicles. An overview of energy saving measures proposed within the rail industry is presented along with a review of different energy storage devices and systems developed for both rail and automotive applications. Advanced flywheels have been identified as a candidate energy storage device for rail applications, combining high specific power and energy. In order to assess the potential benefits of energy storage systems in rail vehicles, a computational model of a conventional regional diesel train has been developed. This has been used to define a base level of vehicle performance, and to investigate the effects of energy efficient control strategies focussing on the application of coasting prior to braking. The impact of these measures on both the requirements of an energy storage system and the potential benefits of a hybrid train have been assessed. A detailed study of a range of existing and novel mechanical flywheel transmissions has been performed. The interaction between the flywheel, transmission and vehicle is investigated using a novel application-independent analysis method which has been developed to characterise and compare the performance of different systems. The results of this analysis produce general ‘design tools’ for each flywheel transmission configuration, allowing appropriate system configurations and parameters to be identified for a particular application. More detailed computational models of the best performing systems have been developed and integrated with the conventional regional diesel train model. The performance of proposed flywheel hybrid regional trains has been assessed using realistic component losses and journey profiles, and the fuel saving relative to a conventional train quantified for a range of energy storage capacities and power-train control strategies