|Abstract (english)|| |
The proposal for a system to increase energy efficiency and railway transport safety is based on procedures focused on specific domains of drive systems and railway infrastructure using structured datasets, indicators and indices as inputs for management of driving missions. The thesis proposes a new model for the management of driving missions, optimized based on the data from remote sensor networks. On the other hand, a hybrid drive with multiple energy sources, that is, an electrical energy storage system, improves the energy efficiency when operating on mountain routes, while the knowledge of a minimal data set describing the condition of the route at a specific location and time interval is required to fulfill the driving mission under adverse weather conditions. Due to the complexity of such a system, computer-aided dynamic management based on efficient mathematical models is required. The input parameters of the hybrid traction drive model are limited to local domain variables, and therefore, the paper proposes a predictive management scheme aimed at simultaneous increase of transport safety. The proposed information collection process is based on expert traffic technology knowledge and technological capabilities of the fourth and fifth generation (4G/5G) sensor networks. The results obtained are used in a simulation environment for the synthesis and validation of the proposed model of railway transport management. This thesis initially proposes a quasi-static model of a conventional diesel - electric locomotive. The proposed model designed based on the known parameters of the locomotive drive assembly, such as the rated power of the diesel engine and the output electric power of the generator unit, and the electrical traction system power input. For the purpose of deriving the fuel consumption model and the related exhaust emissions model, as well as the traction force and traction power characteristics of the propulsion system, the characteristics of a similar diesel engine and generator are matched and their fuel consumption data were taken from the available literature for the purpose of adjustment (scaling) in order to correspond to the power characteristics of the target engine-generator unit of the considered diesel-electric locomotive. Thus-defined conventional locomotive model is then used for the hypothetical conversion of the locomotive into a battery-hybrid and fully-electric battery-based variants by adding a suitable battery energy storage system of sufficiently large capacity, in parallel to the generator in the former case, or standalone in the latter case. In addition, an appropriately optimized energy management strategy has been devised in order to keep the battery state-of-charge within predefined limits, while aiding the conventional propulsion system during its operation, and also facilitating recuperation of a portion of the train potential energy during descending by converting its kinetic energy to electrical energy through regenerative braking by means of traction electrical motors. A new approach to rail freight haul by combined use of conventional diesel-electric and battery-electric locomotives for different characteristic scenarios of individual and joint (tandem) operation is also considered herein. In order to manage power flows between the conventional and battery electric locomotives, and to ensure an additional level of safety aimed at maintaining the battery state-of-charge charge above the minimum recommended value of 20% in demanding traction conditions, the driver’s Notch command reduction rule is also included, which intercepts and conditions the accelerator command set by the driver. Simulation models of battery-electric and conventional diesel-electric locomotives are compared and simulated in different tandem and stand-alone combinations for the considered mountainous rail route and the case of a return journey, including realistic railway slopes and speed limits. The work presented herein also describes the development of the comprehensive model of a battery-hybrid diesel-electric locomotive extended with additional data related to the state of the railway infrastructure, which are obtained from a remote network of wireless sensors based on the application of fourth and fifth generation narrow-band communication technologies. The data used in the model refer to two key parameters that define the track conditions, i.e. the wheel-to-railway coefficient of adhesion and the average head wind speed, which, in turn, correspond to the changing weather conditions on the track. These data are then used within the freight train model to predict its driving dynamics, and are also used to find the optimal parameters of the battery state-of-charge controller for different operating modes, thus avoiding unnecessary and harmful deep battery discharging within the battery-hybrid dieselelectric locomotive. For the selected profile of the mountain railway route managed by the Croatian national railway company (Croatian railways, HŽ), an energy efficiency assessment is also performed, corresponding to a comparative assessment of fuel consumption of conventional and hybrid battery locomotives using a return journey simulation scenario, which includes realistic route slopes and train speed limitations. The obtained simulation results have shown that utilization of a battery-hybrid locomotive can result in significant energy savings and reduction in fuel consumption by up to 16.5%, which is also reflected in the proportional reduction of greenhouse gas emissions. Extended simulation model involving variable track conditions has been able to identify the necessary adjustment of driver behavior (i.e., change of driving mode and need for braking) under variable wheel-to-rails adhesion conditions. These modifications of the locomotive operating mode also resulted in increased travel time under worsened track conditions (compared to the reference case with constant adhesion and head wind) in accordance with the limitations of the locomotive traction system. Deteriorated track conditions (i.e. reduced adhesion and increased head wind) also result in increased fuel consumption and deeper discharge of the battery energy storage system within the battery-hybrid diesel-electric locomotive due to suboptimal operation of the main drive (i.e. diesel engine-generator set and traction electric motors). In particular, in the case of reduced adhesion, the potential for collecting the kinetic energy of the freight train via traction electric motor drive is significantly reduced, while an increase in head wind generally results in increased energy consumption. The presented results have shown that an optimized battery state-of-charge controller that uses track status information from the remote wireless sensors network can maintain the battery state-of-charge above the minimum recommended value of 20%, which has not been the case with the state-of-charge controller with constant parameters. Therefore, the optimized controller is capable of preventing deep discharges of the battery, and, thus to reduce its degradation and the associated reduction of battery life, while maintaining fuel consumption at an acceptable level for the specific highly-demanding driving scenario. The results also indicate that the train model that includes track condition data from remote sensors can predictively determine the duration of a running mission on the railway segment in question and to predict possible unplanned train stops due to unfavorable meteorological conditions on the track. Based on the established correlation of friction coefficient, ambient temperature and absolute air humidity, a fuzzy logic-based algorithm for classifying the state of adhesion between wheels and rails on the railway line has been derived with the aim of warning the driver of changes in braking and traction force potential on the individual parts of the route based on air humidity and temperature measurements from the remote sensors network. The fuzzy route classification algorithm and the appropriate driver’s intervention in the case of reduced braking and traction force potential is facilitated through the reduction of train speed (which inherently also increases the traffic safety and facilitates easier emergency stopping), and the overall concept has been tested by computer simulations. The proposed track condition classification algorithm effectively alerts the driver to timely reduce the train speed on critical sections where poor track adhesion conditions are detected. This is extremely important for the next generation of autonomously driven railway vehicles, which, due to the lack of a driver and his driving experience on a certain traffic route, have to use a predictive sensor network, whose data availability becomes a critical safety factor at higher train speeds. Moreover, such systems with predictive warnings significantly contribute to the reduction of excessive cognitive load of train drivers and increase the safety of the train transportation process, especially in difficult weather and geographical conditions. This ultimately contributes to the overall increased safety of rail traffic in terms of increasing the stability of the railway timetables. The final part of the paper presents a technological and economic analysis conducted under the condition of expected battery life and the associated expected capacity drop of 20% during battery operation, where the hybrid locomotive can maintain the same traction performance as the conventional one. Based on the current installation costs of contemporary lithium-ion batteries, it is expected that the investment costs in hybridization could be recouped approximately three times within the expected battery life. Furthermore, the obtained simulation results for a battery-electric locomotive show that such a locomotive is the most energy efficient solution, but is able to cross the considered mountain route only at very low loads. If additional battery charging is introduced in the middle of the route, the battery-electric locomotive in independent operation can achieve an increase in traction as well as improved driving characteristics. In tandem operations, a battery-electric locomotive can provide significant fuel savings of up to 20% associated with the simultaneous use with the dieselelectric locomotive, especially if a double-charging scenario is considered, which is also reflected in the proportional reduction in greenhouse gases emissions. If electricity costs are also taken into account, conventional tandem configurations plus a battery locomotive can provide savings of up to 22%. While these savings may not be able to justify the estimated cost of a brand new battery-electric locomotive based on the current costs of battery-powered energy storage systems, this may not be the case when considering long-term use, given the current declining trends of battery purchase costs.