Applications & Pathways

Solid oxide fuel cells (SOFC) have developed to a mature technology able to achieve electrical efficiencies beyond 60%. This makes them particularly suitable for off-grid applications where SOFCs can supply both electricity and heat at high efficiency. Concerns related to lifetime particularly when operated dynamically and the high investment cost are however still the main obstacles toward a widespread adoption of this technology. In this paper we propose a hybrid cogeneration system that attempts to overcome these limitations in which the SOFC mainly provides the baseload of the system. Introducing a purification unit allows the production and storage of pure hydrogen from the SOFC anode off-gas. The hydrogen can be stored and used in a proton exchange membrane fuel cell (PEMFC) during peak demands. The SOFC system is completed with a battery used during periods of high electricity production. We propose the use of a mixed integer-linear optimization framework for the sizing of the different components of the system and particularly for identifying the optimal trade-off between round-trip efficiency and investment cost of the battery-based and hydrogen-based storage systems. The proposed system is applied and optimized to two case studies: an off-grid dwelling and a cruise ship. The results show that if the SOFC is used as the main energy conversion technology of the system the use of hydrogen storage in combination with a PEMFC and a battery is more economically convenient compared to the use of the SOFC in stand-alone mode or of pure battery storage. The results show that the proposed hybrid storage solution makes it possible to reduce the investment cost of the system while maintaining the use of the SOFC as the main energy source of the system.

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries these vehicles offer greater efficiency and zero emissions. However their energy management remains a challenge requiring advanced strategies. This paper presents a comparative study of two developed energy management strategies: a deterministic rule-based approach and a fuzzy logic approach. The proposed system consists of a proton exchange membrane fuel cell (PEMFC) as the primary energy source and a lithium-ion battery as the secondary source. A comprehensive model of the hybrid powertrain is developed to evaluate energy distribution and system behaviour. The control system includes a model predictive control (MPC) method for fuel cell current regulation and a PI controller to maintain DC bus voltage stability. The proposed strategies are evaluated under standard driving cycles (UDDS and NEDC) using a simulation in MATLAB/Simulink. Key performance indicators such as fuel efficiency hydrogen consumption battery state-of-charge and voltage stability are examined to assess the effectiveness of each approach. Simulation results demonstrate that the deterministic strategy offers a structured and computationally efficient solution while the fuzzy logic approach provides greater adaptability to dynamic driving conditions leading to improved overall energy efficiency. These findings highlight the critical role of advanced control strategies in improving FCHEV performance and offer valuable insights for future developments in hybrid-vehicle energy management.

This study investigates potential solutions for low-carbon power generation with hydrogen firing and carbon capture. Multi-dimensional system modeling was used to assess the effects on plant performance size and cost. The examined cycles include advanced dry- wet- bottoming- oxyfuel cycles with air-separation units and post-combustion carbon capture with exhaust gas recirculation. The results identify three distinct lowcarbon technology pathways. While conventional combined-cycle plants are suitable for hydrogen retrofits hydrogen firing (both blue and green) results in levelized costs of electricity 50%–300% higher than carbon capture solutions making carbon capture more attractive for long-term energy storage. When carbon capture is applied to conventional combined cycles they become suboptimal compared to alternative solutions. The intercooled-recuperated (ICR) gas turbine cycle integrated with post-combustion carbon capture offers superior performance: over 3% higher efficiency 12% lower capital costs and 70% smaller physical footprint compared to conventional combined cycles with carbon capture. The Allam cycle represents a third pathway achieving 100% CO2 capture with efficiency comparable to combined cycles at 90% capture. Gas separation units emerge as the dominant source of both capital costs and efficiency penalties across all carbon capture configurations representing the key area for future optimization to reduce overall electricity costs.

The global transport sector a significant contributor to energy consumption and greenhouse gas (GHG) emissions requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI’s role in optimizing vehicle energy management traffic flow and alternative fuel technologies such as hydrogen fuel cells and biofuels. It explores AI’s potential to drive advancements in electric and autonomous vehicles shared mobility and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However challenges such as data quality computational demands system integration and ethical concerns must be addressed to fully harness AI’s potential. The study also highlights the policy implications of AI adoption underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness.

Accelerating the transition to a cleaner global energy system is essential for tackling the climate crisis and green hydrogen energy systems hold significant promise for integrating renewable energy sources. This paper offers a thorough evaluation of green hydrogen’s potential as a groundbreaking alternative to achieve near-zero greenhouse gas (GHG) emissions within a renewable energy framework. The paper explores current technological options and assesses the industry’s present status alongside future challenges. It also includes an economic analysis to gauge the feasibility of integrating green hydrogen providing a critical review of the current and future expectations for the levelized cost of hydrogen (LCOH). Depending on the geographic location and the technology employed the LCOH for green hydrogen can range from as low as EUR 1.12/kg to as high as EUR 16.06/kg. Nonetheless the findings suggest that green hydrogen could play a crucial role in reducing GHG emissions particularly in hard-to-decarbonize sectors. A target LCOH of approximately EUR 1/kg by 2050 seems attainable in some geographies. However there are still significant hurdles to overcome before green hydrogen can become a cost-competitive alternative. Key challenges include the need for further technological advancements and the establishment of hydrogen policies to achieve cost reductions in electrolyzers which are vital for green hydrogen production.

The Department of Energy’s (DOE) hydrogen and fuel cell activities are presented focussing on key targets and progress. Recent results on the cost durability and performance of fuel cells are discussed along with the status of hydrogen-related technologies and cross-cutting activities. DOE has deployed fuel cells in key early markets including backup power and forklifts. Recent analyses show that fuel cell electric vehicles (FCEVs) are among the most promising options to reduce greenhouse gas emissions and petroleum use. Preliminary analysis also indicates that the total cost of ownership of FCEVs will be comparable to other advanced vehicle and fuel options.

Energy decarbonisation is essential to achieve Net-Zero emissions goal by 2050. Consequently investments in alternative low-carbon pathways and energy carriers for the heat sector are required. In this study we propose an optimisation framework for the transition of heat sector in Great Britain focusing on hydrogen infrastructure decisions. A spatially-explicit mixed-integer linear programming (MILP) evolution model is developed to minimise the total system’s cost considering investment and operational decisions. The optimisation framework incorporates both long-term planning horizon of 5-year steps from 2035 to 2050 and typical days with hourly resolution. Aiming to alleviate the computational effort of such multiscale model two hierarchical solution approaches are suggested that result in computational time reduction. From the optimisation results it is shown that the installation of gas reforming hydrogen production technologies with CCS and biomass gasification with CCS can provide a cost-effective strategy achieving decarbonisation goal. What-if analysis is conducted to demonstrate further insights for future hydrogen infrastructure investments. Results indicate that as cost is highly dependent on natural gas price Water Electrolysis capacity increases significantly when gas price rises. Moreover the introduction of carbon tax policy can lead to lower CO2 net emissions.

The work carried out in this paper focused on “Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines”. The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the turbulent flame speed for a spark-ignition internal combustion engine operating with a hydrogen-natural gas mixture then evaluate the relevance of these models in relation to the usual approaches. The novelty of this work is the possibility of a direct calculation of turbulent combustion speed with a good precision using only machine learning model. The obtained models are also compared to each other by considering in turn as a comparison criterion: the precision of the result calculation time and the ability to assimilate original data (which has not undergone preprocessing). An important particularity of this work is that the input variables of the machine learning models were chosen among the variables directly measurable experimentally based on the opinion of experts in combustion in internal combustion engines and not on the usual approaches to dimensionality reduction on a dataset. The data used for this work was taken from a MINSEL 380 a 380-cc single-cylinder engine. The results show that all the machine learning models obtained are significantly faster than the usual approach and Random Forest (R2: R-squared = 0.9939 and RMSE: Root Mean Square Error = 0.4274) gives the best results. With a forecasting accuracy of over 90 % both approaches can make reasonable predictions for most industrial applications such as designing engine monitoring and control systems firefighting systems simulation and prototyping tools.

Nowadays several technologies based on powertrain electrification and the exploitation of hydrogen represent valuable options for decarbonizing the on-road public transport sector. The considered alternatives should exhibit an effective benchmark between CO2 reduction potential and production/operational costs. Conducting a comprehensive Total Cost of Ownership (TCO) analysis coupled with a thorough Life Cycle Assessment (LCA) is therefore crucial in shaping the future for cleaner urban mobility. From this perspective this study compares different powertrain configurations for a 12 m urban bus: a conventional diesel Internal Combustion Engine Vehicle (ICEV) a series hybrid diesel two hydrogen-based series hybrid vehicles: a Hydrogen Hybrid Electric Vehicle featuring an H2-ICE (H2-HEV) or a Fuel Cell Electric Vehicle (FCEV) and a Battery Electric Vehicle (BEV). Moreover a sensitivity analysis has been conducted on the carbon footprint for power generation considering also the marginal electricity mix. In addition prospective LCA and TCO elements are introduced by addressing future technological projections for the 2030 horizon. The research reveals that as of today the BEV and hydrogen-fueled vehicles have comparable environmental impacts when the marginal electricity mix is considered. The techno-economic analysis indicates that under current conditions FCEVs and H2-HEVs are not cost-effective for CO₂ reduction unless powered by renewable energy sources. However considering future technological advancements and market evolution FCEVs offer the most promising balance between economic and environmental benefits particularly if hydrogen prices reach €4 per kilogram. If hydrogen-powered vehicles remain a niche market BEVs will be the most viable option for decarbonizing the transport sector in most European countries.

Green hydrogen production and storage are vital in mitigating carbon emissions and sustainable transition. However the high investment cost and management requirements are the bottleneck of utilizing hybrid hydrogen-based systems in microgrids. Given the necessity of cost-effective and optimal design of these systems the present study examines techno-economic feasibility of integrating hybrid hydrogen-based systems into an outdoor test facility. With this perspective several solar-driven hybrid scenarios are introduced at two energy storage levels namely the battery and hydrogen energy storage systems including the high-pressure gaseous hydrogen and metal hydride storage tanks. Dynamic simulations are carried out to address subtle interactions in components of the hybrid system by establishing a TRNSYS model coupled to a Fortran code simulating the metal hydride storage system. The OpenStudio-EnergyPlus plugin is used to simulate the building load validate against experimental data according to the measured data and monitored operating conditions. Aimed at enabling efficient integration of energy storage systems a techno-enviro-economic optimization algorithm is developed to simultaneously minimize the levelized cost of the electricity and maximize the CO2 mitigation in each proposed hybrid scenario. The results indicate that integrating the gaseous hydrogen and metal hydride storages into the photovoltaic-alone scenario enhances 22.6% and 14.4% of the annual renewable factor. Accordingly the inclusion of battery system to these hybrid scenarios gives a 30.4% and 20.3 % boost to the renewable factor value respectively. Although the inclusion of battery energy storage into the hybrid systems increases the renewable factor the results imply that it reduces the hydrogen production rate via electrolysis. The optimized values of the levelized cost of electricity and CO2 emission for different scenarios vary in the range of 0.376–0.789 $/kWh and 6.57–9.75 ton respectively. The multi-criteria optimizations improve the levelized cost of electricity and CO2 emission by up to 46.2% and 11.3% with respect to their preliminary design.