Applications & Pathways

Polymer Electrolyte Membrane fuel cell (PEMFC) uses hydrogen as fuel to generate electricity and by-product water at relatively low operating temperatures which is environmentally friendly. Since PEMFC performance characteristics are inherently nonlinear and related predicting the best performance for the different operating conditions is essential to improve the system’s efficiency. Thus modeling using artificial neural networks (ANN) to predict its performance can significantly improve the capabilities of handling multi-variable nonlinear performance of the PEMFC. This paper predicts the electrical performance of a PEMFC stack under various operating conditions. The four input terms for the 5 W PEMFC include anode and cathode pressures and flow rates. The model performances are based on ANN using two different learning algorithms to estimate the stack voltage and power. The models have shown consistently to be comparable to the experimental data. All models with at least five hidden neurons have coefficients of determination of 0.95 or higher. Meanwhile the PEMFC voltage and power models have mean squared errors of less than 1 × 10−3 V and 1 × 10−3 W respectively. Therefore the model results demonstrate the potential use of ANN into the implementation of such models to predict the steady state behavior of the PEMFC system (not limited to polarization curves) for different operating conditions and help in the optimization process for achieving the best performance of the system.

The European steel industry aims at a CO2 reduction of 80–95% by 2050 ensuring that Europe will meet the requirements of the Paris Agreement. As the reduction potentials of the current steelmaking routes are low the transfer toward breakthrough-technologies is essential to reach these goals. Hydrogen-based steelmaking is one approach to realize CO2-lean steelmaking. Therefore the natural gas (NG)-based direct reduction (DR) acts as a basis for the first step of this transition. The high flexibility of this route allows the gradual addition of hydrogen and in a long-term view runs the process with pure hydrogen. Model-based calculations are performed to assess the possibilities for injecting hydrogen. Therefore NG- and hydrogen-based DR models are developed to create new process know-how and enable an evaluation of these processes in terms of energy demand CO2-reduction potentials and so on. The examinations show that the hydrogen-based route offers a huge potential for green steelmaking which is strongly depending on the carbon footprint of the electricity used for the production of hydrogen. Only if the carbon intensity is less than about 120 g CO2 kWh1 the hydrogen-based process emits less CO2 than the NG-based DR process.

An energy transition is in progress around the globe notably led by an increase in the deployment of renewable energy and a shift toward less emissions-intense options notably in the transportation sector. This research investigates the potential role that new transportation options namely flying vehicles may play toward progressing the energy transition. As flying vehicles are a relatively new technology yet to penetrate the market it is also prudent to consider the ethical legal and social issues (ELSI) associated with their implementation alongside the potential energy and environmental impacts. Through a review of ELSI and energy and environmental literature we identify research gaps and identify how flying vehicles may impact upon the energy transition over time. Our research identifies several critical aspects of both ELSI and energy and environmental academia relevant to the future deployment of flying vehicles and describes a deployment timeline and the resultant societal outcomes. We find that flying vehicles could drive the energy transition and the hydrogen economy and that their widespread adoption could engender shared socio-environmental benefits. Our findings are relevant to transportation and environmental policymakers and identify critical considerations for the planned introduction of new shared transportation options to the market conducive to a sustainable energy transition.

The European Union expects that hydrogen will play a vital role in future energy systems. Fuel cell electric vehicles currently present a key development path for electrification of the transport sector which requires infrastructure investments of hydrogen refueling stations preferably powered by renewables such as solar and wind energy. The economic feasibility of refueling stations depends on geographical locations. This study introduces a model to identify the key cost components of renewable hydrogen for refueling stations and simulates the performance using solar radiation wind speed and electricity price data in a selection of Swedish cities. The study demonstrates the importance of integrating the electricity grid in green hydrogen production. Wind speed is crucial in reducing the cost whereas solar radiation has less influence. In addition a combination of solar and wind brings better performance in an off-grid scenario. The most encouraging finding is the cost of 35e72 SEK/kg (3.5e7.2 V/kg) which is competitive with reported costs in other EUcountries especially since this cost excludes any government support scheme. The study provides a reference for investors and policy makers foreseeing the industrial landscape for hydrogen energy development.

To evaluate the functional reliability of the pyrotechnic igniter in the failure mode of unstable combustion at low temperature a reliability and reliability sensitivity analysis method based on the combination of an interior ballistic model and Kriging reliability method is proposed. Through the deterministic interior ballistic simulation the failure mode of low temperature unstable combustion of the pyrotechnic igniter is examined while the random variables are introduced to establish the ignition nonlinear implicit function of the pyrotechnic igniter. The ignition display function of the pyrotechnic igniter is established by the Kriging model which avoids the repeated calculation of true limit state function values. This study provides an efficient approach to evaluate the ignition reliability of the pyrotechnic igniter and compared with the traditional Monte Carlo method to verify the accuracy of the results. Finally reliability-based sensitivity indices are presented to quantify the significance of random parameters. It is shown that the influence of the uncertainties can be precisely described and the diameter of the nozzle plays a dominant role in ignition reliability. Additionally ignition experiments of nozzles with different diameters were performed to verify the result of sensitivity. This can further support the detailed design of the pyrotechnic igniter

Hydrogen is a promising zero-carbon fuel for internal combustion engines; however the geometric optimization of injectors for low-pressure direct-injection (LPDI) systems under lean-burn conditions remains underexplored. This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories ensuring accurate prediction of in-cylinder flow structures. The optimization focused on two critical geometric parameters: injector hole count and injection angle. Partial indicated mean effective pressure (pIMEP) and in-cylinder NOx emissions were selected as conflicting objectives to balance performance and emissions. Adaptive mesh refinement (AMR) was employed to resolve transient in-cylinder flow and combustion dynamics with high spatial accuracy. Among 22 evaluated configurations including both capped and uncapped designs the injector featuring three holes at a 15.24◦ injection angle outperformed the baseline delivering improved mixture uniformity reduced knock tendency and lower NOx emissions. These results demonstrate the potential of geometrybased optimization for advancing hydrogen-fueled LPDI engines toward cleaner and more efficient combustion strategies.

A numerical study was conducted to investigate the effects of hydrogen and scavenging air temperature (SAT) on the combustion and emission characteristics of a 2-stroke heavy-duty dual-fuel (DF) marine engine at full load. The engine had a 700 mm bore fuelled with hydrogen–methane (H2-CH4) mixtures. Three-dimensional simulations of the combustion and emission formation inside the engine cylinder with various H2 contents in the H2-CH4 mixture were performed. ANSYS FLUENT simulation software was used to analyse the engine performance in-cylinder pressure temperature and emission characteristics. The CFD models were validated against the measured data recorded from the engine experiments. The results showed that an increase in the in-cylinder peak pressure increased the engine power when the H2 content in the H2-CH4 mixture increased. Notably CO2 and soot emissions decreased (up to more than 65%) when the H2 content in the gaseous mixture increased to 50%. Specific NO emissions in the DF modes were lower than that of the diesel mode when the H2 content in the gaseous mixture was lower than 40%. However they increased compared to the diesel mode when the H2 content continued to increase. This limits the H2 amount that should be used in a gaseous mixture creating NO emissions. The results also showed that the SAT cooling method can further reduce emission problems while enhancing engine power. In particular reducing the SAT to 28 ◦C in the gaseous mixture with 10% H2 ensured that the DF mode emitted the lowest NO emissions compared to the diesel mode. This reduced NO emissions by 37.92% compared to the measured NO emissions of the research engine (a Tier II marine engine). This study successfully analysed the benefits of using an H2-CH4 mixture as the primary fuel and the SAT cooling method in a 2-stroke ME-GI heavy-duty marine engine.

Hydrogen fuel cell technology is securing a place in the future of advanced mobility and the energy revolution as engineers explore multiple paths in the quest for decarbonization. The feasibility of hydrogen-based fuel cell vehicles particularly relies on the development of safe lightweight and cost-competitive solutions for hydrogen storage. After the demonstration of hundreds of prototype vehicles today commercial hydrogen tanks are in the first stages of market introduction adopting configurations that use composite materials. However production rates remain low and costs high. This paper intends to provide an insight into the evolving scenario of solutions for hydrogen storage in the transportation sector. Current applications in different sectors of transport are covered focusing on their individual requirements. Furthermore this work addresses the efforts to produce economically attractive composite tanks discussing the challenges surrounding material choices and manufacturing practices as well as cutting-edge trends pursued by research and development teams. Key issues in the design and analysis of hydrogen tanks are also discussed. Finally testing and certification requirements are debated once they play a vital role in industry acceptance.

Vehicles using a single fuel cell as a power source often have problems such as slow response and inability to recover braking energy. Therefore the current automobile market is mainly dominated by fuel cell hybrid vehicles. In this study the fuel cell hybrid commercial vehicle is taken as the research object and a fuel cell/ battery/supercapacitor energy topology is proposed and an energy management strategy based on a doubledelay deep deterministic policy gradient is designed for this topological structure. This strategy takes fuel cell hydrogen consumption fuel cell life loss and battery life loss as the optimization goals in which supercapacitors play the role of coordinating the power output of the fuel cell and the battery providing more optimization ranges for the optimization of fuel cells and batteries. Compared with the deep deterministic policy gradient strategy (DDPG) and the nonlinear programming algorithm strategy this strategy has reduced hydrogen consumption level fuel cell loss level and battery loss level which greatly improves the economy and service life of the power system. The proposed EMS is based on the TD3 algorithm in deep reinforcement learning and simultaneously optimizes a number of indicators which is beneficial to prolong the service life of the power system.

This paper presents a greedy energy management strategy based on model predictive control (MPC) for a stand-alone microgrid powered by photovoltaic (PV) arrays and equipped with batteries and a power-to-hydrogen-to-power (P2H2P) system. The proposed strategy consists of a day-ahead plan and an intra-day dispatch method. In the planning stage the sequence of plan is to determine the power of each storage device for a certain period which is initially generated under the principle that PV arrays have the highest priority followed by the batteries and finally the P2H2P system using short-term forecast data of both load and solar irradiance. The initial plan can be optimized with objectives of harvesting more PV generation in storage and minimizing unmet load through rescheduling P2H2P system and batteries. Three parameters including reserved capacity of batteries predischarge coefficient of fuel cell (FC) and greedy coefficient of electrolyzer (EL) are introduced during plan optimization process to enhance the robustness against forecast errors. In the dispatching stage the energy dispatch is subject to the scheduled plan and the operational constraints. To demonstrate the capabilities of the proposed strategy a case study is performed for a hotel with a mean power consumption of 1567 kWh/day based on the system configuration optimized by HOMER software in comparison with the load following (LF) strategy and the global optimum solution solved by mixed integer linear programing (MILP). The simulation results show that the annual unmet load using the proposed strategy is reduced from 13434 kWh to 2370 kWh which is 528 kWh lower than the optimum solution. Meanwhile the cost of energy (COE) of the proposed strategy decreases by US$ 0.08/kWh compared to the LF strategy and is equal to the optimum solution. Finally the performance of configuration optimization employing genetic algorithm (GA) under different energy management strategies is investigated with the objective function of minimizing the net present cost (NPC). Furthermore the robustness of the proposed strategy is studied. The results show that the proposed strategy gives an NPC and COE of US$ 2.4 million (Mn) and US$ 0.43/kWh which are 23.4% and 9.7% lower than those of systems utilizing the SoC-based strategy and the LF strategy respectively. The results also demonstrate that the strategy is robust against forecast errors especially for overestimated forecast models.