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

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

Using information engineering to understand the impact of train positioning uncertainties on railway subsystems

Many studies propose new advanced railway subsystems, such as Driver Advisory System (DAS), Automatic Door Operation (ADO) and Traffic Management System (TMS), designed to improve the overall performance of current railway systems. Real time train positioning information is one of the key pieces of input data for most of these new subsystems. Many studies presenting and examining the effectiveness of such subsystems assume the availability of very accurate train positioning data in real time. However, providing and using high accuracy positioning data may not always be the most cost-effective solution, nor is it always available. The accuracy of train position information is varied, based on the technological complexity of the positioning systems and the methods that are used. In reality, different subsystems, henceforth referred to as ‘applications’, need different minimum resolutions of train positioning data to work effectively, and uncertainty or inaccuracy in this data may reduce the effectiveness of the new applications. However, the trade-off between the accuracy of the positioning data and the required effectiveness of the proposed applications is so far not clear. A framework for assessing the impact of uncertainties in train positions against application performance has been developed. The required performance of the application is assessed based on the characteristics of the railway system, consisting of the infrastructure, rolling stock and operational data. The uncertainty in the train positioning data is considered based on the characteristics of the positioning system. The framework is applied to determine the impact of the positioning uncertainty on the application’s outcome. So, in that way, the desired position resolution associated with acceptable application performance can be characterised. In this thesis, the framework described above is implemented for DAS and TMS applications to understand the influence of positioning uncertainty on their fundamental functions compared to base case with high accuracy (actual position). A DAS system is modelled and implemented with uncertainty characteristic of a Global Navigation Satellite System (GNSS). The train energy consumption and journey time are used as performance measures to evaluate the impact of these uncertainties compared to a base case. A TMS is modelled and implemented with the uncertainties of an on-board low-cost low-accuracy positioning system. The impact of positioning uncertainty on the modelled TMS is evaluated in terms of arrival punctuality for different levels of capacity consumption. The implementation of the framework for DAS and TMS applications determines the following: • which of the application functions are influenced by positioning uncertainty; • how positioning uncertainty influences the application output variables; • how the impact of positioning uncertainties can be identified, through the application output variables, whilst considering the impact of other railway uncertainties; • what is the impact of the underperforming application, due to positioning uncertainty, on the whole railway system in terms of energy, punctuality and capacity

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Optimization of Energy-Efficient Speed Profile for Electrified Vehicles

This work presents a study of the energy-efficient operation of all-electric vehicles leveraging route information, such as road grade, to adjust the velocity trajectory Minimization of energy consumption is one of the main targets of research for both passenger vehicles in terms of economic benefit, and army vehicles in terms of mission success and decision making. The optimization of a speed profile is one of the tools used to achieve energy minimization and it can also help in the useful utilization of autonomy in vehicles. The optimization of speed profile is typically addressed as an Optimal Control Problem (OCP). The obstacle that disrupts the implementation of optimization is the heavy computational load that results from the number of state variables, control inputs, and discretization, i.e., the curse of dimensionality. In this work, Pontryagin's Maximum Principle (PMP) is applied to derive necessary conditions and to determine the possible discrete operating modes. The analysis shows that only five modes are required to achieve minimum energy consumption; full propulsion, cruising, coasting, full regeneration, and full braking. Then, the problem is reformulated and solved in the distance domain using Dynamic Programming to find the optimal speed profiles. Various simulation results are shown for a lightweight autonomous military vehicle. Army Programs use various drive cycles including time, speed, and grade, for testing and validating new vehicle systems and models. Among those cycles, two different drive conditions are studied: relatively flat, Convoy, and hilly terrain, Churchville B. For the Convoy cycle, the optimal speed cycle uses 21% less energy for the same trip duration or reduces the time by 14% with the same energy consumption while for the Churchville B cycle, it uses 24% less energy or provides 24% reduction in time. Furthermore, the sensitivity of energy consumption to regenerative-braking power limits and trip time is investigated. These studies provide important information that can be used in designing component size and scheduling operation to achieve the desired vehicle range. Lastly, the work provides parametric studies about the influence of the efficiency of an electric motor on performance including energy consumption and control modes.Master of Science in EngineeringAutomotive Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/146793/1/Hadi_Abbas_Thesis (1).pdfDescription of Hadi_Abbas_Thesis (1).pdf : Thesi

Aspekte der Verkehrstelematik – ausgewählte Veröffentlichungen 2015

Mit dem sechsten Band der Schriftenreihe Verkehrstelematik wird ein Überblick über die intermodalen Forschungsthemen des Jahres 2015 der Professur für Verkehrsleitsysteme und ‑prozessautomatisierung der Fakultät Verkehrswissenschaften „Friedrich List“ der Technischen Universität Dresden anhand ausgewählter Veröffentlichungen gegeben. Sieben ausgewählte Artikel der Mitarbeiter, hauptsächlich veröffentlicht im Rahmen nationaler und internationaler Konferenzen, wurden dafür zusammengestellt. Die ersten Schwerpunkte bilden dabei die energieoptimale Steuerung und das Verkehrsmanagement im Schienenverkehr. Hier wird der Frage nachgegangen, wie Störungen des Bahnbetriebs im Echtzeit-Betriebsmanagement mit mathematischen Methoden begegnet werden kann. Als ein Ansatzpunkt wird das Erzeugen von robusten, stabilen und dabei auch energieeffizienten Fahrplänen diskutiert. Weiterhin wird versucht, im Rahmen des Betriebsmanagements mittels Konfliktlösungsalgorithmen operativ aktualisierte Fahrpläne so aufzubereiten, dass eine Umsetzung mit fahrzeugseitigen Fahrerassistenzsystemen ermöglicht und ein energieeffizienter Betrieb sichergestellt ist. Im zweiten Teil des Bandes wird gezeigt, wie die Methoden und Algorithmen der energieoptimalen Fahrweise und eines entsprechenden Fahrerassistenzsystems auf die Straßenbahn und auch den Bus übertragen werden können. Anschließend wird gänzlich auf den Individualverkehr fokussiert und der Frage der Reichweitenoptimierung elektrischer Fahrzeuge durch energieeffiziente Routing-Algorithmen unter Berücksichtigung von Echtzeit-Verkehrslagedaten nachgegangen. Wie im Schienenverkehr wird das Finden der optimalen Fahrstrategie auch hier durch Fahrerassistenzsysteme unterstützt

System energy optimisation strategies for DC railway traction power networks

Energy and environmental sustainability in transportation are becoming ever more important. In Europe, the transportation sector is responsible for about 32% of the final energy consumption. Electrified railway systems play an important role in contributing to the reduction of energy usage and C0$_2$ emissions compared with other transport modes. Previous studies have investigated train driving strategies for traction energy saving. However, few of them consider the overall system energy optimisation. This thesis analyses the energy consumption of urban systems with regenerating trains, including the energy supplied by substations, used in power transmission networks, consumed by monitoring trains, and regenerated by braking trains. This thesis proposes an approach to searching energy-efficient driving strategies with coasting controls. A Driver Advisory System is designed and implemented in a field test on Beijing Yizhuang Subway Line. The driver guided by the DAS achieves 16% of traction energy savings, compared with normal driving. This thesis also proposes an approach to global system energy consumption optimisation, based on a Monte Carlo Algorithm. The case study indicates that the substation energy is reduced by around 38.6% with the system optimised operations. The efficiency of using regenerative braking energy is improved to from 80.6 to 95.5%

An experimental analysis of hierarchical rail traffic and train control in a stochastic environment

The hierarchical connection of Rail Traffic Management System (TMS) and Automatic Train Operation (ATO) for mainline railways has been proposed for a while; however, few have investigated this hierarchical connection with the real field. This paper studies in detail the benefits and limitations of an integrated framework of TMS and ATO in stochastic and dynamic conditions in terms of punctuality, energy efficiency, and conflict-resolving. A simulation is built by interfacing a rescheduling tool and a stand-alone ATO tool with the realistic traffic simulation environment OpenTrack. The investigation refers to different disturbed traffic scenarios obtained by sampling train entrance delays and dwell times within a typical Monte Carlo scheme. Results obtained for the Dutch railway corridor Utrecht–Den Bosch prove the value of the approach. In case of no disruptions, the implementation of ATO systems is beneficial for maintaining timetables and saving energy costs. In case of delay disruptions, the TMS rescheduling has its full effect only if trains are able to follow TMS rescheduled timetables, while the energy-saving by using ATO can only be achieved with conflict-free schedules. A bi-directional communication between ATO and TMS is therefore beneficial for conflict-resolving and energy saving