While the largely electrified rail network allows for direct utilization of renewable energy sources, there is still a considerable share of diesel-powered trains operating on non- and partly electrified tracks. To replace these, the more sustainable alternatives such as battery electric railway vehicles need to present a viable option with sufficient range. This paper aims to adapt and improve an existing optimization algorithm, previously used with diesel-powered trains, for the operation of battery electric railway vehicles. In this new approach, battery control is optimized alongside train control, utilizing a direct method solver to find the minimum energy trajectory. Furthermore, a detailed train model is implemented that is designed for operation on partly electrified tracks. To yield a highly accurate, yet also sufficiently fast algorithm, a numerical analysis is conducted and the parameters of the algorithm are determined accordingly. Finally, the application of the adapted algorithm on a use case in Germany shows that both velocity profile and control adapt in a way that minimizes utilization of the battery. The results indicate that the proposed algorithm presents a reliable and robust method to obtain minimum energy controls for battery electric railway vehicles with any electrification pattern.

Today’s railway network capacity is limited by constraints imposed by traditional train protection systems. A way to overcome those limitations, maximize the railway network performance and also increase the operational flexibility is presented by the Virtually Coupled Train Set (VCTS) concept. This paper evaluates the technical feasibility of this approach, that was developed and is further evaluated in the framework of the Shift2Rail (S2R) project X2Rail-3. The main functionality of virtually coupled train sets is achieved by replacing the mechanical coupler between two railway vehicles by an electronic (virtual) coupling link. This operational change requires a permanent vehicle-to-vehicle communication and precise distance measurement, while enabling much faster coupling and decoupling procedures, increased interoperability and the operation of trains with a headway below absolute braking distance. To evaluate the technical feasibility of the VCTS concept, a series of technical and operational subsystem have been identified and analyzed. Interviews with experts from a variety of VCTS linked topics have been conducted, to evaluate the state of the art and new developments for those subsystems. Subsequently, the capabilities of the subsystems have been compared with the requirements of the VCTS system. In addition, different mitigations to overcome possible obstacles have been identified and evaluated. As the result, the most critical technical aspects for the implementation and success of VCTS have been identified as the requirement of controllable, fast and accurate responding braking systems, the availability of suitable communication technologies and frequency bands, the need for highly-accurate measurement of distance, speed and acceleration and the fast detection and monitoring of train integrity. Considering those results, a qualitative roadmap for the future VCTS development and introduction strategy is derived.

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Battery electric multiple units (BEMU) are an effective path towards a decarbonized regional rail transport on partly electrified rail lines. As a means of sector coupling, the BEMU recharging energy demand provided through overhead line islands can be covered from decentralized renewable energy sources (RES). Thus, fully carbon-free electricity for rail transport purposes can be obtained. In this study, we analyze cost reduction potentials of efficient recharging infrastructure positioning and the feasibility of covering BEMU energy demand by direct-use of locally produced renewable electricity. Therefore, we set up a model-based approach which assesses relevant lifecycle costs (LCC) of different trackside electrification alternatives comparing energy supply from local RES and grid consumption. The model-based approach is applied to the example of a German regional rail line. In the case of an overhead line island, the direct-use of electricity from adjacent wind power plants with on-site battery storage results in relevant LCC of EUR 173.4 M/30a, while grid consumption results in EUR 176.2 M/30a whereas full electrification results in EUR 224.5 M/30a. Depending on site-specific factors such as existing electrification and line lengths, BEMU operation and partial overhead line extension can lead to significant cost reductions of recharging infrastructure as compared to full electrification.

While the electrified rail network can directly utilize renewable energy sources, track electrification is costly and subject to environmental and structural limitations. Therefore, research is currently underway on alternative propulsion systems that enable overhead line-free operation. As a promising solution, the fuel cell electric drive came into focus as an emission-free drive system at the point of use. In order to be able to present a cost-efficient substitution of the propulsion systems in use today, an intensive examination of energy-optimal operating patterns, i.e. minimal fuel consumption, is crucial. This is the basis of this work, as it aims to develop an optimization algorithm that can handle fuel cell hybrid electric powertrains in a flexible and robust manner. The developed algorithm allows simultaneous optimization of the speed trajectory and on-board energy management with the aim of reducing hydrogen consumption. A comparison is made between a rule-based approach and the optimization algorithm. By simultaneously optimizing the trajectory and power distribution, 16% of the hydrogen savings potential can be achieved on a regional route in Germany compared to the rule-based approach. Finally, an in-depth evaluation of the algorithm’s ability to flexibly handle different fuel cell hybrid powertrain topologies is performed. The results show that the optimization algorithm opens up the possibility of evaluating reasonable fuel cell hybrid component sizes while achieving optimal operation. Thus, it can be used in the future to support feasibility analysis for specific use cases.