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

Multi-objective optimisation for battery electric vehicle powertrain topologies

Electric vehicles are becoming more popular in the market. To be competitive, manufacturers need to produce vehicles with a low energy consumption, a good range and an acceptable driving performance. These are dependent on the choice of components and the topology in which they are used. In a conventional gasoline vehicle, the powertrain topology is constrained to a few well-understood layouts; these typically consist of a single engine driving one axle or both axles through a multi-ratio gearbox. With electric vehicles, there is more flexibility, and the design space is relatively unexplored. In this paper, we evaluate several different topologies as follows: a traditional topology using a single electric motor driving a single axle with a fixed gear ratio; a topology using separate motors for the front axle and the rear axle, each with its own fixed gear ratio; a topology using in-wheel motors on a single axle; a four-wheel-drive topology using in-wheel motors on both axes. Multi-objective optimisation techniques are used to find the optimal component sizing for a given requirement set and to investigate the trade-offs between the energy consumption, the powertrain cost and the acceleration performance. The paper concludes with a discussion of the relative merits of the different topologies and their applicability to real-world passenger cars

A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Energy efficiency and integration of urban electrical transport systems: EVS and metro-trains of two real European lines

Transport is a main source of pollutants in cities, where air quality is a major concern. New transport technologies, such as electric vehicles, and public transport modalities, such as urban railways, have arisen as solutions to this important problem. One of the main difficulties for the adoption of electric vehicles by consumers is the scarcity of a suitable charging infrastructure. The use of the railway power supplies to charge electric vehicle batteries could facilitate the deployment of charging infrastructure in cities. It would reduce the cost because of the use of an existing installation. Furthermore, electric vehicles can use braking energy from trains that was previously wasted in rheostats. This paper presents the results of a collaboration between research teams from University of Rome Sapienza and Comillas Pontifical University. In this work, two real European cases are studied: an Italian metro line and a Spanish metro line. The energy performance of these metro lines and their capacity to charge electric vehicles have been studied by means of detailed simulation tools. Their results have shown that the use of regenerated energy is 98% for short interval of trains in both cases. However, the use of regenerated energy decreases as the train intervals grow. In a daily operation, an important amount of regenerated energy is wasted in the Italian and Spanish case. Using this energy, a significant number of electric vehicles could be charged every day

Slip and Adhesion in a Railway Wheelset Simulink Model Proposed for Detection Driving Conditions Via Neural Networks

Constantly enlarging operation of locomotives with a very high tractive power in modern railway transport has caused problems with optimal supplying torque from motor to wheel-sets. Losses emerging with inadequate torque values lead to wheel slipping connected with excessive wear and limited acceleration. In models simulating dynamics of torque transmission from the drive units to wheels, the most important are the submodel of the drive and the submodel of balance between traction forces and drive resistances. Some issues of this field studied within a PhD program and SGS (CTU Students Grant Competition) has been focused on increasing quality of these submodels. This contribution is aimed at an innovated part in the existing Simulink model utilizing new data sources and modeling techniques. This improvement supports application of operating point detection methods based on machine learning techniques. New control facilities provided with pulse-width modulated frequency control of the asynchronous motor will be used for automatic submission of optimal operating points. The idea of utilization of via simulation obtained data is an on-line training of polynomial neural unit as an approximation of current driving conditions.Neustále narůstající provoz lokomotiv s velmi vysokým trakčním výkonem v moderní železniční dopravě způsobuje problémy s přenosem optimálního hnacího momentu z motoru na dvojkolí. Ztráty vyplývající z nevhodných hodnot točivého momentu vedou k prokluzu kol spojeným s nadměrným opotřebením a omezeným zrychlením. V modelech simulujících dynamiku přenosu točivého momentu z pohonné jednotky na dvojkolí jsou nejdůležitější submodely pohonu a rovnováhy mezi trakčními silami a jízdními odpory. Výzkum prováděný v rámci doktorských studijních programů a SGS (Studentská grantová soutěž ČVUT) se zaměřuje na zvyšování kvality těchto submodelů. Tento příspěvek je zaměřen na inovovanou část v existujícím Simulink modelu využívajícím nové zdroje dat a technik modelování. Nové možnosti regulace zajištěné pulzně-šířkovou frekvenční regulací asynchronního motoru budou použity pro automatické poskytnutí optimálních provozních bodů. Představa využití simulací získaných dat je on-line učení polynomické neuronové jednotky jako aproximace současných jízdních podmínek

Automatic Train Operation Speed Profile Optimization and Tracking with Multi-Objective in Urban Railway

Besides energy-efficiency, people also want train operation to be comfortable, punctual and parking precise. In this paper, a multi-objective model for automatic train operation in urban railway is proposed by unifying dimensions of different objectives firstly. This model is built by applying multi-objective decision with the penalty function, based on the analysis of train performance and its operation environment. Then a genetic algorithm is developed to solve this model and obtain the optimal recommended speed profiles. Thirdly, fuzzy controller is designed to achieve track recommended speed profiles. Finally, with the help of Matlab software, control effect is verified based on simulation. From the simulation results, it can be seen this strategy can meet the requirement of multi-objective, which are energy-saving, parking precisely, running punctually and comfort

Optimization models of rail transportation under the financial crisis

This paper proposes an analysis of the most used models to optimize the rail transportation. Are presented a series of optimization models of labor efficiency in this sector, but also elements that gives the information on the competitiveness of this mode of transport.railway, railway optimization, optimization models for railway

Energy Management Systems for Smart Electric Railway Networks: A Methodological Review

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated