Publications

Proton exchange membrane (PEM) based electrochemical systems have the capability to operate in fuel cell (PEMFC) and water electrolyser (PEMWE) modes enabling efficient hydrogen energy utilisation and green hydrogen production. In addition to the essential cell stacks the system of PEMFC or PEMWE consists of four sub-systems for managing gas supply power thermal and water respectively. Due to the system’s complexity even a small fluctuation in a certain sub-system can result in an unexpected response leading to a reduced performance and stability. To improve the system’s robustness and responsiveness considerable efforts have been dedicated to developing advanced control strategies. This paper comprehensively reviews various control strategies proposed in literature revealing that traditional control methods are widely employed in PEMFC and PEMWE due to their simplicity yet they suffer from limitations in accuracy. Conversely advanced control methods offer high accuracy but are hindered by poor dynamic performance. This paper highlights the recent advancements in control strategies incorporating machine learning algorithms. Additionally the paper provides a perspective on the future development of control strategies suggesting that hybrid control methods should be used for future research to leverage the strength of both sides. Notably it emphasises the role of artificial intelligence (AI) in advancing control strategies demonstrating its significant potential in facilitating the transition from automation to autonomy.

Innovative green vehicle concepts have become increasingly prevailing in consumer purchasing habits as technology evolves. The global transition towards sustainable transportation indicates an increase in new-generation vehicles including both fuel-cell electric vehicles (FCEVs) and plug-in electric vehicles (PEVs) that will take on roads in the future. This change requires new-generation stations to support electrification. This study introduced a prominent multi-energy system concept with a hydrogen refueling station. The proposed multi-energy system (MES) consists of green hydrogen production a hydrogen refueling station for FCEVs hydrogen injection into natural gas (NG) and a charging station for PEVs. An on-site renewable system projected at the station and a polymer electrolyte membrane electrolyzer (PEM) to produce hydrogen for two significant consumers support MES. In addition the MES offers the ability to conduct two-way trade with the grid if renewable energy systems are insufficient. This study develops a comprehensive multi-energy system with an economically optimized energy management model using a mixed-integer linear programming (MILP) approach. The determinative datasets of vehicles are generated in a Python environment using Gauss distribution. The fleet of FCEVs and PEVs are currently available on the market. The study includes fleets of the most common models from well-known brands. The results indicate that profits increase when the storage capacity of the hydrogen tank is higher and natural gas injections are limitless. Optimization results for all cases tend to choose higher-priced natural gas injections over hydrogen refueling because of the difference in costs of refueling and injection expenses. The analyses reveal the highest hydrogen sales to the natural gas (NG) grid by consuming 2214.31 kg generating a revenue of $6966 and in contrast the lowest hydrogen sales to the natural gas grid at 1045.38 kg resulting in a revenue of $3286. Regarding electricity the highest sales represent revenue of $7701 and $2375 for distribution system consumption and electric vehicles (EV) respectively. Conversely Cases 1 and 2 have achieved sales to EV of $2286 and $2349 respectively but do not have any sales to distribution system consumption regarding the constraints. Overall the optimization results show that the solution is optimal for a multi-energy system operator to achieve higher profits and that all end-user parties are satisfied.

Due to the climate crisis and the restriction measures taken in the last decade electric buses are gaining popularity in the transport sector. However one of the most significant disadvantages of this type of vehicle is its low autonomy. Many electric buses with proton-exchange membrane fuel cells (PEMFC) systems have been developed to solve this problem in recent years. These have an advantage over battery-electric buses because the autonomy depends on the capacity of the hydrogen tanks. As with batteries thermal management is crucial for fuel cells to achieve good performance and prolong service life. For this reason it is necessary to investigate different strategies or configurations of a fuel cell electric bus’s integral thermal management system (ITMS). In the present work a novel global model of a fuel cell electric bus (FCEB) has been developed which includes the thermal models of the essential components. This model was used to evaluate different strategies in the FCEB integrated thermal management system simulating driving cycles of the public transport system of Valencia Spain under winter weather conditions. The first strategy was to use the heat generated by the fuel cell to heat the vehicle’s cabin achieving savings of up to 7%. The second strategy was to use the waste heat from the fuel cells to preheat the batteries. It was found that under conditions where a high-power demand is placed on the fuel cell it is advisable to use the residual heat to preheat the battery resulting in an energy saving of 4%. Finally a hybrid solution was proposed in which the residual heat from fuel cells is used to heat both the cabin and the battery resulting in an energy saving of 10%.

The use of energy in the built environment contributes to over one-third of the world’s carbon emissions. To reduce that effect two primary solutions can be adopted i.e. (i) renovation of old buildings and (ii) increasing the renewable energy penetration. This review paper focuses on the latter. Renewable energy sources typically have an intermittent nature. In other words it is not guaranteed that these sources can be harnessed on demand. Thus complement solutions should be considered to use renewable energy sources efficiently. Hydrogen is recognized as a potential solution. It can be used to store excess energy or be directly exploited to generate thermal energy. Throughout this review various research papers focusing on hydrogen-based heating systems were reviewed analyzed and classified from different perspectives. Subsequently articles related to machine learning models optimization algorithms and smart control systems along with their applications in building energy management were reviewed to outline their potential contributions to reducing energy use lowering carbon emissions and improving thermal comfort for occupants. Furthermore research gaps in the use of these smart strategies in residential hydrogen heating systems were thoroughly identified and discussed. The presented findings indicate that the semi-decentralized hydrogen-based heating systems hold significant potential. First these systems can control the thermal demand of neighboring homes through local substations; second they can reduce reliance on power and gas grids. Furthermore the model predictive control and reinforcement learning approaches outperform other control systems ensuring energy comfort and cost-effective energy bills for residential buildings.

Green hydrogen has become a central topic in discussions about the global energy transition seen as a promising solution for decarbonizing economies and meeting climate goals. As part of the process of decarbonization green hydrogen can replace fossil fuels currently in use helping to reduce emissions in sectors vital to the global economy such as industry and transport as well as in the power and heat sectors. Whilst there is significant potential for green hydrogen there are also challenges. The upfront costs for infrastructure and technology are high and the availability and accessibility of the renewables needed for production varies by region. Green hydrogen production and storage technologies are continuously evolving and being promoted as the demand for hydrogen in many applications grows. Considering this this paper presents the main methods for its production and storage as well as its economic impact. Hence the trend of governments and international organizations is to invest in research and development to make this technology more accessible and efficient given the carbon reduction targets.

Achieving the required decarbonisation targets by the shipping industry requires a transition to technologies with zero or near-zero greenhouse gas (GHG) emissions. One promising shipping fuel with zero emission of exhaust gases (including CO2) is green hydrogen. This type of fuel recognised as a 100% clean solution is being investigated for feasible use on a service offshore vessel (SOV) working for offshore wind farms. This study aims to examine whether hydrogen may be used on an SOV in terms of the technical and economic challenges associated with the design process and other factors. In the analyses a reference has been made to the current International Maritime Organization (IMO) guidelines and regulations. In this study it was assumed that hydrogen would be directly combusted in a reciprocating internal combustion engine. This engine type was reviewed. In further research hydrogen fuel cell propulsion systems will also be considered. The hydrogen demand was calculated for the assumed data of the SOV and then the volume and number of highpressure tanks were estimated. The analyses revealed that the SOV cannot undertake 14-day missions using hydrogen fuel stored in cylinders on board. These cylinders occupy 66% of the ship’s current volume and their weight including the modular system accounts for 62% of its deadweight. The costs are over 100% higher compared to MDO and LNG fuels and 30% higher than methanol. The actual autonomy of the SOV with hydrogen fuel is 3 days.

To achieve sustainable development the transition from a fossil-based economy to a circular economy is essential. The use of renewable energy sources to make the overall carbon foot print more favorable is an important pre-requisite. In this context it is crucial to valorize all renewable resources through an optimized local integration. One opportunity arises through the synergy between bioresources and green hydrogen. Through techno-economic assessments this work analyzes four local case studies that integrate bio-based processes with green hydrogen produced via electrolysis using renewable energy sources. An analysis of the use of webGIS tools (i.e. Atlas of Biorefineries of IEA Bioenergy) to identify existing biorefineries that require hydrogen in relation to territories with a potential availability of green hydrogen has never been conducted before. This paper provides an evaluation of the production costs of the target products as a function of the local green hydrogen supply costs. The results revealed that the impact of green hydrogen costs could vary widely ranging from 1% to 95% of the total production costs depending on the bio-based target product evaluated. Additionally hydrogen demand in the target area could require an installed variable renewable energy capacity of 20 MW and 500 MW. On the whole the local integration of biorefineries and green hydrogen could represent an optimal opportunity to make hydrogenated bio-based products 100% renewable.

This study presents a method for predicting nozzle surface temperature and the timing of frost formation during hydrogen refueling using machine learning. A continuous refueling system was implemented based on a simulation model that was developed and validated in previous research. Data were collected under various boundary conditions and eight regression models were trained and evaluated for their predictive performance. Hyperparameter optimization was performed using random search to enhance model performance. The final models were validated by applying boundary conditions not used during model development and comparing the predicted values with simulation results. The comparison revealed that the maximum error rate occurred after the second refueling with a value of approximately 4.79%. Currently nitrogen and heating air are used for defrosting and frost reduction which can be costly. The developed machine learning models are expected to enable prediction of both frost formation and defrosting timings potentially allowing for more cost-effective management of defrosting and frost reduction strategies.

An integrated renewable system that utilizes solid waste-based biogas is important steps towards the sustainable energy solutions to rural off-grid communities in Bangladesh. In this study a hybrid energy system consisting of photovoltaic modules wind turbines biogas generators fuel cells and electrolyzer-hydrogen tank-based energy storage is optimized using non-dominated sorting genetic algorithm (NSGA-II). The hybrid system is optimized based on the cost of energy and human health damage as objective functions and a fuzzy decision-making technique is employed to determine the optimal solution to the multi-objective approach. Additionally several economic ecological and social indicators are also investigated while meeting a certain load reliability. An energy management strategy has been developed in the MATALB environment to satisfy the community load and the battery-driven electric vehicle load. Results from this comprehensive analysis suggest that the optimal configuration of PV/WT/FC/BG has an energy cost of 0.1634 $/kWh and an ecosystem damage of 0.00098 species.year. The human health damage and the human development index of the optimized system are 0.1732 DALYs and 0.696 DALYs respectively. Additionally the proposed system has a lifecycle emission of 123730 kg CO2-eq/year carbon emission penalties of $1856/year a job creation potential of 30 jobs/MW over the 25 years of project lifetime. The hybrid system oversees solid waste management solutions and provides the community with sustainable energy and vehicle recharge.

This paper provides an in-depth analysis of alternative fuels including liquefied natural gas (LNG) hydrogen ammonia and biofuels assessing their feasibility based on operational requirements availability safety concerns and the infrastructure needed for large-scale adoption. Moreover it examines hybrid and fully electric propulsion systems considering advancements in battery technology and the integration of renewable energy sources such as wind and solar power to further reduce SOV emissions. Key findings from this research indicate that LNG serves as a viable short- to medium-term solution for reducing GHG emissions in the SOV sector due to its relatively lower carbon content compared to MDO and HFO. This paper finally insists that while LNG presents an immediate opportunity for emission reduction in the SOV sector a combination of hydrogen ammonia and hybrid propulsion systems will be necessary to meet long-term decarbonisation goals. The findings underscore the importance of coordinated industry efforts technological innovation and supportive regulatory frameworks to overcome the technical economic and infrastructural challenges associated with decarbonising the maritime industry.