Electric Battery System Research Articles

Applied Metal Hydride System Optimization to Improve Hydrogen Fuel Cell System Performance in Mobile Heavy-Duty Applications

Hydrogen fuel cells and hydrogen internal combustion engines face challenges in competing with battery electric systems in mobile applications due to their lower overall system efficiency. Evaluating the impact of various technologies on the system level is crucial for assessing their economic and ecological potential.Thermodynamic constraints, such as Carnot efficiency, inherently limit the efficiency of these systems. To address this, efforts to enhance the efficiency of fuel cells and internal combustion engines must explore untapped potentials, such as utilizing pressure energy between pressurized tanks and fuel cell systems, which is about 15 % of the higher heating value of the hydrogen stored in the 700 bar tanks.One promising approach involves employing an open metal hydride refrigeration system, utilizing pressure energy through alternating exothermic hydrogen absorption and endothermic desorption in metal hydride reactors. This system provides quasi-continuous cooling power for climatizing train cabins, drivers’ cabs, and refrigerated trailers (see Fig. 1). A lab-scale prototype, currently at TRL 4, demonstrates this quasi-continuous cooling power with steady-state hydrogen mass flows and an electrochemical efficiency of 81 %.With this prototype, efficiency improvements were achieved through optimized valve switching times (Weckerle et al. 2020).However, further optimization is warranted, particularly through simulation-based techniques leveraging realistic driving patterns of heavy-duty vehicles. Here we present the validation of a metal hydride simulation model with control strategy enhancements, evaluated under heavy-duty applications such as hydrogen regional trains and refrigeration trucks.Our simulation model, validated by repeated experiment analyses within a 95% confidence interval, facilitates optimization of heat transfer fluid and hydrogen flow, resulting in longer desorption cycles and higher efficiency of 86 – 91 % with the lab-scale prototype as a reference. The reactors are modeled as plate heat exchangers with a heat source in Matlab Simulink, utilizing a first-order reaction approach based on isothermal pressure-temperature characterization of the material. Optimization of desorption cycles, incorporating variations in heat transfer fluid flow, absorption and desorption cycle durations, and hydrogen mass flow through bypass valves, is performed via scripted methods.Extended desorption cycles significantly reduce switching cycles (see Fig. 2), with the main performance impact attributed to the heating or cooling requirements of the reactor's lump heat mass during transitions. The integration of this improved control strategy with existing vehicle models enables a more realistic assessment of system performance compared to current practices.Even modest improvements, such as a 5 % points efficiency enhancement, can lead to significant energy savings, exemplified by a reduction of 370 kWh/a in the annual energy consumption of a hydrogen regional train (Kordel et al. 2024). This study emphasizes the importance of application efficiency and energy consumption as key indicators for enhancing the competitiveness of mobile hydrogen applications.The presentation will delve into the validation process, optimization workflow and application-specific results and show the impact on the efficiency of hydrogen trains and refrigerated trailers. With this research, we aim to bridge the gap between technological advances and real-world applications in hydrogen science. Literature: Kordel, Markus; Heeland, Matthew Maikel; Knetsch, Kevin (2024): IRSA 2023 Proceedings: Waste Energy AC Technologies in H2-Multiple Units. In: Nils Nießen und Christian Schindler (Hg.): IRSA 2023 : Tagungsband/proceedings: Aachen, Germany 22-23 November 2023, S. 561–581. Online verfügbar unter https://elib.dlr.de/202183/.Weckerle, C.; Nasri, M.; Hegner, R.; Bürger, I.; Linder, M. (2020): A metal hydride air-conditioning system for fuel cell vehicles – Functional demonstration. In: Applied Energy 259, S. 114187. DOI: 10.1016/j.apenergy.2019.114187. Acknowledgement: The author would like to acknowledge the ESCALATE project (Grant Agreement No: 101096598), which the European Union funds under the Horizon Research and Innovation Programs. Figure 1

Applied Metal Hydride System Optimization to Improve Hydrogen Fuel Cell System Performance in Mobile Heavy-Duty Applications

Hydrogen fuel cell vehicles face challenges in competing against battery electric systems as their efficiency is 25 % lower. Especially in the heavy-duty sector the efficiency of fuel cell electric vehicles needs to be increased accordingly, as this market will play a major role in future. One promising approach could be to decrease the energy demand of refrigerated trucks by the pressure energy between the hydrogen tank and fuel cell in metal hydrides to provide cooling power. However, the cooling power output is not constant and further efficiency improvements can be done. Here we show a simulative state space controller optimization which can provide a constant power output and can increase the efficiency by 8 % points compared to an uncontrolled system. A future combination with state-of-the-art efficiency improvement measures can potentially increase the system efficiency even further.

Model-Based Evaluation of Energy Systems for Multirotor UAV Based on Batteries and Fuel Cells

In this study, two multirotor unmanned aerial vehicle (UAV) energy systems are comparatively evaluated via a modelling process, namely a battery electric system and a hybrid fuel cell and battery system. Technical, economic, and ecological evaluation parameters are considered. The evaluation is performed on three different mission types: a 10 km delivery mission, a 3000 m2 facade inspection, and a nine-minute drone show, to represent the transport, monitoring, and event sectors. Results are calculated via a modelling process first simulating the required power profiles for each mission and then simulating both energy systems’ operational behavior during each mission. The resulting technical parameters show that the battery electric UAV has 1.7 times higher efficiency than the fuel cell hybrid UAV. However, the fuel cell hybrid UAV allows potential flight durations 3.1 times longer than the battery electric UAV. Considering economic parameters, the battery electric UAV is the more economical choice due to the higher investment costs of the fuel cell hybrid UAV, even when considering future cost developments for investment and energy costs. For ecological parameters, the fuel cell hybrid UAV has the potential to produce significantly less emissions, but only when using hydrogen produced from renewable energy (green hydrogen). All in all, the battery electric UAV is sufficient for the three concrete missions considered and should be chosen over the fuel cell hybrid UAV. However, the fuel cell hybrid UAV should be considered for missions with longer required flight times than the battery electric UAV is capable of, especially in the transport and monitoring sectors.

Fuel cell electric vehicles: An option to decarbonize heavy-duty transport? Results from a Swiss case-study

CO2 emissions of road freight transport may seem secondary to passenger cars, but electrification could eliminate direct emissions of cars. For heavy-duty trucks, it is unclear if substituting Diesel is even an option. We developed a data-driven approach to explore this issue: it estimates feasibility considering the daily operation patterns of every vehicle in the fleet. This paper presents results for fuel cell propulsion systems. If every Swiss truck drove on hydrogen produced exclusively by electrolysis, full decarbonisation would draw over 8TWh of renewable electricity (13% of the national consumption). That corresponds to roughly 60 km2 of photovoltaic panels with 1.5GW peak power. We found that current fuel-cell technology almost completely realized that potential, provided vehicles could refuel during the day. The autonomy range was generally better than with battery electric systems without significant weight increase (relative to the original vehicle). Refuelling could take over half an hour, requiring a dense energy infrastructure, able to refuel hundreds of vehicles in parallel to avoid congestion (i.e. vehicles waiting). The reduction of direct emissions was easily overcompensated by indirect emissions of generation: the Swiss consumer mix lead to virtually no overall reduction, while natural gas powerplants lead to a significant CO2 increase. We concluded that hydrogen is technically a very attractive decarbonisation agent for heavy-duty vehicles, but significant investments may be required to ensure that (a) hydrogen production is truly renewable and (b) vehicles have adequate access to additional energy during the day.

A Two‐Stage Stochastic Mixed‐Integer Programming Approach to the Smart House Scheduling Problem

SUMMARYA “smart house” is a highly energy‐optimized house equipped with photovoltaic (PV) systems, electric battery systems, fuel cell (FC) cogeneration systems, electric vehicles (EVs), and so on. Smart houses are attracting much attention recently because of their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC cogeneration system, and an EV. We formulate the problem as a mixed‐integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand, and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real‐life demands and PV power generation to show the effectiveness of our method. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 48–58, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22336

リコース付き確率混合整数計画法によるスマートハウスの運用最適化

SUMMARY A “smart house” is a highly energy-optimized house equipped with photovoltaic (PV) systems, electric battery systems, fuel cell (FC) cogeneration systems, electric vehicles (EVs), and so on. Smart houses are attracting much attention recently because of their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC cogeneration system, and an EV. We formulate the problem as a mixed-integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand, and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real-life demands and PV power generation to show the effectiveness of our method. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 48–58, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22336