Egypt

This paper presents a new Fuel Cell Fuel Consumption Minimization Strategy (FCFCMS) for Hybrid Electric Vehicles (HEVs) powered by a fuel cell and an energy storage system in order to minimize as much as possible the consumption of hydrogen while maintaining the State Of Charge (SOC) of the battery. Compared to existing Energy Management Strategies (EMSs) (such as the well-known State Machine Strategy (SMC) Fuzzy Logic Control (FLC) Frequency Decoupling and FLC (FDFLC) and the Equivalent Consumption Minimization Strategy (ECMS)) the proposed strategy increases the overall vehicle energy efficiency and therefore minimizes the total hydrogen consumption while respecting the constraints of each energy and power element. A model of a hybrid vehicle has been built using the TruckMaker/MATLAB software. Using the Urban Dynamometer Driving Schedule (UDDS) which includes several stops and accelerations the performance of the proposed strategy has been compared with these different approaches (SMC FLC FDFLC and ECMS) through several simulations.

DFT calculations at B3LYP/6-31g(dp) with the D3 version of Grimme’s dispersion are performed to investigate the application of TM-encapsulated Mg12O12 nano-cages (TM= Mn Fe and Co) as a hydrogen storage material. The molecular dynamic (MD) calculations are utilized to examine the stability of the considered structures. TD-DFT method reveals that the TM-encapsulation converts the Mg12O12 from an ultraviolet into a visible optical active material. The adsorption energy values indicate that the Mn and Fe atoms encapsulation enhances the adsorption of H2 molecules on the Mg12O12 nano-cage. The pristine Mg12O12 and CoMg12O12 do not meet the requirements for hydrogen storage materials while the MnMg12O12 and FeMg12O12 obey the requirements. MnMg12O12 and FeMg12O12 can carry up to twelve and nine H2 molecules respectively. The hydrogen adsorption causes a redshift for the λmax value of the UV-Vis. spectra of the MnMg12O12 and FeMg12O12 nano-cages. The thermodynamic calculations show that the hydrogen storage reaction for MnMg12O12 nano-cage is a spontaneous reaction while for FeMg12O12 nano-cage is not spontaneous. The results suggested that the MnMg12O12 nano-cage may be a promising material for hydrogen storage applications.

The use of hybrid renewable energy systems (HRES) has become the best option for supplying electricity to sites remote from the central power system because of its sustainability environmental friendliness and its low cost of energy compared to many conventional sources such as diesel generators. Due to the intermittent nature of renewable energy resources there is a need however for an energy storage system (ESS) to store the surplus energy and feed the energy deficit. Most renewable sources used battery storage systems (BSS) a green hydrogen storage system (GHSS) and a diesel generator as a backup for these sources. Batteries are very expensive and have a very short lifetime and GHSS have a very expensive initial cost and many security issues. In this paper a system consisting of wind turbines and a photovoltaic (PV) array with a pumped hydro energy storage (PHES) system as the main energy storage to replace the expensive and short lifetime batteries is proposed. The proposed system is built to feed a remote area called Dumah Aljandal in the north of Saudi Arabia. A smart grid is used via a novel demand response strategy (DRS) with a dynamic tariff to reduce the size of the components and it reduces the cost of energy compared to a flat tariff. The use of the PHES with smart DRS reduced the cost of energy by 34.2% and 41.1% compared to the use of BSS and GHSS as an ESS respectively. Moreover the use of 100% green energy sources will avoid the emission of an estimated 2.5 million tons of greenhouse gases every year. The proposed system will use a novel optimization algorithm called the gradually reduced particles of particle swarm optimization (GRP-PSO) algorithm to enhance the exploration and exploitation during the searching iterations. The GRP-PSO reduces the convergence time to 58% compared to the average convergence time of 10 optimization algorithms used for comparison. A sensitivity analysis study is introduced in this paper in which the effect of ±20% change in wind speed and solar irradiance are selected and the system showed a low effect of these resources on the Levelized cost of energy of the HRES. These outstanding results proved the superiority of using a pumped-storage system with a dynamic tariff demand response strategy compared to the other energy storage systems with flat-rate tariffs.

This work reports on H2 fuel generation from sewage water using Cu/CuO nanoporous (NP) electrodes. This is a novel concept for converting contaminated water into H2 fuel. The preparation of Cu/CuO NP was achieved using a simple thermal combustion process of Cu metallic foil at 550 ◦C for 1 h. The Cu/CuO surface consists of island-like structures with an inter-distance of 100 nm. Each island has a highly porous surface with a pore diameter of about 250 nm. X-ray diffraction (XRD) confirmed the formation of monoclinic Cu/CuO NP material with a crystallite size of 89 nm. The prepared Cu/CuO photoelectrode was applied for H2 generation from sewage water achieving an incident to photon conversion efficiency (IPCE) of 14.6%. Further the effects of light intensity and wavelength on the photoelectrode performance were assessed. The current density (Jph) value increased from 2.17 to 4.7 mA·cm−2 upon raising the light power density from 50 to 100 mW·cm−2 . Moreover the enthalpy (∆H*) and entropy (∆S*) values of Cu/CuO electrode were determined as 9.519 KJ mol−1 and 180.4 JK−1 ·mol−1 respectively. The results obtained in the present study are very promising for solving the problem of energy in far regions by converting sewage water to H2 fuel.

The world is facing a challenge in meeting its needs for energy. Global energy consumption in the last half-century has increased very rapidly and is expected to continue to grow over the next 50 years. However it is expected to see significant differences between the last 50 years and the next. This paper aims at introducing a good solution to replace or work with conventional marine power plants. This includes the use of fuel cell power plant operated with hydrogen produced through water electrolysis or hydrogen produced from natural gas gasoline or diesel fuels through steam reforming processes to mitigate air pollution from ships.

In this paper a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules Biomass gasifier (BG) Electrolyzer units Hydrogen Tank units (HT) and Fuel Cell (FC) system. A recent optimization algorithm namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA the obtained results are compared with other well-known optimization algorithms namely Sooty Tern Optimization Algorithm (STOA) Whale Optimization Algorithm (WOA) and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 $/kWh the net present cost (NPC) with 6170134 $ the loss of power supply probability (LPSP) with 0.05993% and GHG with 792.534 t/y.

The Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) is the key part to enhance optimal power distribution. Indeed the most recent works are focusing on optimizing hydrogen consumption without taking into consideration the degradation of embedded energy sources. In order to overcome this lack of knowledge this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC) which aims to minimize the battery degradation to extend its lifetime. In this proposed algorithm the health-conscious EMS is normalized in order to address its multi-objective optimization. Then weighting factors are assigned in the objective function to minimize the selected criteria. Compared to most EMSs based on optimization techniques this proposed approach does not require any information about the speed profile which allows it to be used for real-time control of FCHEV. The achieved simulation results show that the proposed approach reduces the economic cost up to 50% for some speed profile keeping the battery pack in a safe range and significantly reducing energy sources degradation. The proposed health-conscious EMS has been validated experimentally and its online operation ability clearly highlighted on a PEMFC delivery postal vehicle.

Integrating the exergy and economic analyses of water electrolyzers is the pivotal way to comprehend the interplay of system costs and improve system performance. For this a 3D numerical model based on COMSOL Multiphysics Software (version 5.6 COMSOL Stockholm Sweden) is integrated with the exergy and exergoeconomic analysis to evaluate the exergoeconomic performance of the proton exchange membrane water electrolysis (PEMWE) under different operating conditions (operating temperature cathode pressure current density) and design parameter (membrane thickness). Further the gas crossover phenomenon is investigated to estimate the impact of gas leakage on analysis reliability under various conditions and criteria. The results reveal that increasing the operating temperature or decreasing the membrane thickness improves both the efficiency and cost of hydrogen exergy while increasing the gas leakage through the membrane. Likewise raising the current density and the cathode pressure lowers the hydrogen exergy cost and improves the economic performance. The increase in exergy destroyed and hydrogen exergy cost as well as the decline in second law efficiency due to the gas crossover are more noticeable at higher pressures. As the cathode pressure rises from 1 to 30 bar at a current density of 10000 A/m2 the increase in exergy destroyed and hydrogen exergy cost as well as the decline in second law efficiency are increased by 37.6 kJ/mol 4.49 USD/GJ and 7.1% respectively. The cheapest green electricity source which is achieved using onshore wind energy and hydropower reduces hydrogen production costs and enhances economic efficiency. The growth in the hydrogen exergy cost is by about 4.23 USD/GJ for a 0.01 USD/kWh increase in electricity price at the current density of 20000 A/m2. All findings would be expected to be quite useful for researchers engaged in the design development and optimization of PEMWE.

This review critically analyses various aspects of the most promising thermochemical cycles for clean hydrogen production. While the current hydrogen market heavily relies on fossil-fuel-based platforms the thermochemical water-splitting systems based on the reduction-oxidation (redox) looping reactions have a significant potential to significantly contribute to the sustainable production of green hydrogen at scale. However compared to the water electrolysis techniques the thermochemical cycles suffer from a low technology readiness level (TRL) which retards the commercial implementation of these technologies. This review mainly focuses on identifying the capability of the state-of-the-art thermochemical cycles to deploy large-scale hydrogen production plants and their techno-economic performance. This study also analyzed the potential integration of the hybrid looping systems with the solar and nuclear reactor designs which are evidenced to be more cost-effective than the electrochemical water-splitting methods but it excludes fossil-based thermochemical processes such as gasification steam methane reforming and pyrolysis. Further investigation is still required to address the technical issues associated with implementing the hybrid thermochemical cycles in order to bring them to the market for sustainable hydrogen production.

The use of green hydrogen as a fuel source for marine applications has the potential to significantly reduce the carbon footprint of the industry. The development of a sustainable and cost-effective method for producing green hydrogen has gained a lot of attention. Water electrolysis is the best and most environmentally friendly method for producing green hydrogen-based renewable energy. Therefore identifying the ideal operating parameters of the water electrolysis process is critical to hydrogen production. Three controlling factors must be appropriately identified to boost hydrogen generation namely electrolysis time (min) electric voltage (V) and catalyst amount (µg). The proposed methodology contains the following two phases: modeling and optimization. Initially a robust model of the water electrolysis process in terms of controlling factors was established using an adaptive neuro-fuzzy inference system (ANFIS) based on the experimental dataset. After that a modern pelican optimization algorithm (POA) was employed to identify the ideal parameters of electrolysis duration electric voltage and catalyst amount to enhance hydrogen production. Compared to the measured datasets and response surface methodology (RSM) the integration of ANFIS and POA improved the generated hydrogen by around 1.3% and 1.7% respectively. Overall this study highlights the potential of ANFIS modeling and optimal parameter identification in optimizing the performance of solar-powered water electrocatalysis systems for green hydrogen production in marine applications. This research could pave the way for the more widespread adoption of this technology in the marine industry which would help to reduce the industry’s carbon footprint and promote sustainability.