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 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.

The power required for heating, ventilation and air-conditioning (HVAC) of the passenger compartment in BEMU strongly affects the capacity and lifetime of the battery. Accordingly, 11 different measures for reducing the energy demand of HVAC are presented in this paper. The measures are investigated using Dymola models of a thermal car body to determine the energy saving potential as well as the effects on the vehicle. Reducing the amount of fresh air while complying with CO₂ limits and using a heat pump are efficient measures to reduce the annual energy demand for the HVAC system by 62% or 48%, respectively. In addition, a control-based, catenary dependent HVAC operating strategy is being developed that decisively reduces the load on the battery during dynamic operation. This approach is particularly suitable for straightforward implementation since no design changes to the vehicle are necessary.

SEnSOR (Smart Energy Speed Optimizer Rail) is a direct method based optimization algorithm developed at DLR for determining minimum energy speed trajectories for railway vehicles. This paper aims to reduce model error and improve this algorithm for any alternative powertrain architecture. Model simplifications such as projecting the efficiency maps of different train components onto one-dimensional space can lead to inaccuracies and non-optimalities in reality. In this work, 2D section-wise Chua functional representation was used to capture the complete behavior of efficiency maps and discuss its benefits. For this purpose, a new smoothing method was developed. It was observed that there is an average of 6% error in the energy calculation when both, 1D and 2D, models are compared against each other. Previously, solving for different powertrain architectures was time consuming with the requirement of manual modifications to the optimization problem. With a modular approach, the algorithm was modified to flexibly adapt the problem formulation to automatically take into account any changes in powertrain architectures with minimum user input. The benefit is demonstrated by performing optimization on a bi-mode train with three different power sources as developed within the EU-project FCH2RAIL. The advanced algorithm is now capable to adapt to such complex architectures and provide feasible optimization results within a reasonable time.

Battery-electric rail vehicles are a sustainable alternative for diesel-powered rail vehicles operating on tracks without catenary. Heating, ventilation and air conditioning units for those vehicles use typically synthetic refrigerants, which are harmful to the environment. Additionally, the energy demand to heat and cool the cabin is limiting vehicle range and flexibility in operation. Therefore, the presented paper studies how energy demand and load at the traction battery in battery-electric rail vehicles can be reduced using heat pumps with natural refrigerants and an efficient thermal management strategy.A heat pump and thermal car body model are developed and validated, which calculate the electrical energy demand for heating, ventilation and air conditioning in battery-electric rail vehicles. In those models, an optimized thermal management strategy is implemented which changes the cabin setpoint temperature based on catenary availability.For a 2-car battery-electric rail vehicle in climate zone II the annual thermal energy demand is up to 110 MWh. The application of a heat pump with propane as refrigerant can reduce the annual electrical energy demand for heating and cooling by up to 55 % based on the modeled COP curves. The load at the battery can be mitigated further with the optimized thermal management strategy, reducing the equivalent full cycles by 3 %.Overall, the heat pump operation and efficient thermal management strategy lead to a higher vehicle range and flexibility in daily operation, increasing the user acceptance of battery-electric rail vehicles at rail operators and leading to more sustainable public rail transport.

Hydrogen-powered trains present a sustainable alternative to diesel-powered trains for railway transport of passengers and freight. Hydrogen as an energy carrier is cost-intensive, which is why energy demand should be reduced in all relevant operational scenarios. Since thermal losses are one of the main reasons for efficiency reductions, the presented work investigated the research question: Can hydrogen-powered trains combined with targeted energy management strategies be simulated and potential energy efficiency gains be quantified for European and North American scenarios? A digital railway vehicle simulation framework is developed, which models longitudinal dynamics and energy management. The framework is built with standardized functional mock-up interfaces (FMI) for component sub-models to enable a co-simulation with models from different entities. As input for the simulations, different passenger and freight transport scenarios from Europe and North America are used. Requirements for hydrogen-powered trains vary greatly for different use cases of passenger and freight transport, as well as the location with its surrounding climate. Recuperation of braking energy in freight transport is challenging and remains an opportunity for further enhancement. For passenger transport, metal hydride cooling systems can provide additional cooling capacity and reduce electrical energy demand for air conditioning.