Effect of water distribution in gas diffusion layer on proton exchange membrane fuel cell performance

Water management of proton exchange membrane fuel cells remains a prominent issue in research concerning fuel cells. In this study, the gas diffusion layer (GDL) of a fuel cell is partially treated with a hydrophobic agent, and the effect of GDL hydrophobicity on the water distribution in the fuel cell is examined. First, the effect of the position of the cathode GDL hydrophobic area relative to the channel on the fuel cell performance is investigated. Then, the water distribution in the fuel cell cathode GDL is observed using X-ray imaging. The experimental results indicate that when the hybrid GDL's hydrophobic area lies on the channel, water tends to accumulate under the rib, and the water content in the channel is low; this improves the fuel cell performance. When the hydrophobic area is under the rib, the water distribution is more uniform, but the performance deteriorates.     

Integrated standalone hybrid solar PV,fuel cell and diesel generator power system for battery or supercapacitor storage systems in Khorfakkan,United Arab Emirates

Renewable energy resources play a very important rule these days to assist the conventional energy systems for doing its function in the UAE due to high greenhouse gas (GHG) emissions and energy demand. In this paper, the analysis and performance of integrated standalone hybrid solar PV, fuel cell and diesel generator power system with battery energy storage system (BESS) or supercapacitor energy storage system (SCESS) in Khorfakkan city, Sharjah were presented. HOMER Pro software was used to model and simulate the hybrid energy system (HES) based on the daily energy consumption for Khorfakkan city. The simulation results show that using SCESS as an energy storage system will help the performance of HES based on the Levelized cost of energy (LCOE) and greenhouse gas (GHG) emissions. The HES with SCESS has renewable fraction (68.1%) and 0.346 $/kWh LCOE. The HES meets the annual AC primary load of the city (13.6 GWh) with negligible electricity excess and with an unmet electrical load of 1.38%. The reduction in GHG emissions for HES with SCESS was 83.2%, equivalent to saving 814,428 gallons of diesel.     

Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine.     

Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning

In this work, a deep learning accelerated homogenization framework is developed for prediction of elastic modulus of porous materials directly from their inner microstructures. The finite element method (FEM) and the homogenization theory are used to obtain the macroscopic properties of materials based on their microstructures. Based on a large dataset consisting of various microstructures and corresponding elastic properties via FEM, a deep convolutional neural network (CNN) is trained to capture the nonlinear functional relationship between the microstructure features and their macroscopic elastic properties. The deep learning model is finally well validated against extra new samples with excellent predictive performances. This demonstrates that the CNN deep learning model can be trusted as a surrogate model for the FEM based homogenization method, with the computation time being reduced by several orders of magnitude. The proposed deep learning framework is highly extendable for prediction of various macroscopic properties from microstructures.     

Data-driven parameterization of polymer electrolyte membrane fuel cell models via simultaneous local linear structured state space identification

In order to mitigate the degradation and prolong the lifetime of polymer electrolyte membrane fuel cells, advanced, model-based control strategies are becoming indispensable. Thereby, the availability of accurate yet computationally efficient fuel cell models is of crucial importance. Associated with this is the need to efficiently parameterize a given model to a concise and cost-effective experimental data set. A challenging task due to the large number of unknown parameters and the resulting complex optimization problem. In this work, a parameterization scheme based on the simultaneous estimation of multiple structured state space models, obtained by analytic linearization of a candidate fuel cell stack model, is proposed. These local linear models have the advantage of high computational efficiency, regaining the desired flexibility required for the typically iterative task of model parameterization. Due to the analytic derivation of the local linear models, the relation to the original parameters of the non-linear model is retained. Furthermore, the local linear models enable a straight-forward parameter significance and identifiability analysis with respect to experimental data. The proposed method is demonstrated using experimental data from a 30 kW commercial polymer electrolyte membrane fuel cell stack.     

面向平板结构钙钛矿太阳能电池的金属氧化物综述

作为平板结构钙钛矿太阳能电池的电荷传输层，金属氧化物薄膜对器件性能有重要影响. 系统性概述平板结构钙钛矿太阳能电池对金属氧化物薄膜形貌、电学、光学、化学及热等物理特性要求，并对目前在高效钙钛矿太阳电池制备中最有前景的金属氧化物电子传输层及空穴传输层材料特性及代表性工作进行总结. 针对大多数金属氧化物迁移率低、表面缺陷多及能级匹配差的问题，分析元素掺杂、表面改性、复合薄膜设计等手段解决的相关进展. 总结目前金属氧化物薄膜沉积技术现状及优缺点，探讨今后薄膜沉积技术发展、改进方向. 对低温沉积金属氧化物薄膜在柔性器件方面的应用进行展望.     

Dynamics and control of a thermally self-sustaining energy storage system using integrated solid oxide cells for an islanded building

A solid oxide cell-based energy system is proposed for a solar-powered stand-alone building. The system is comprised of a 5 kW_el solid oxide fuel cell (SOFC), a 9.5 kW_el solid oxide electrolysis cell (SOEC), and the required balance of plant. The SOFC supplies: 1- building demand in the absence of sufficient solar power, 2- heat for SOEC in endothermic and standby modes. Thermal integration of SOFC and SOEC is implemented through a network of heat exchangers, combined with set of control algorithms. Two control strategies were implemented to actuate the SOFC in response to endothermic heat demands of SOEC by manipulating: 1- electric power, 2- fuel utilization. The results of dynamic simulation of system for two scenarios (sunny day and cloudy day) showed successful compliance of temperature constraints with both methods. Manipulation of fuel utilization, however, resulted in better system performance in terms of efficiency and H₂ balance.     

Open-circuit voltage exceeding the outermost HOMO-LUMO offset in cascade organic solar cells

We demonstrate a planar organic solar cell with a four-layer cascade architecture that exhibits an open-circuit voltage (Voc) greater than the offset in energy between the highest occupied molecular orbital (HOMO) of the outermost donor and the lowest unoccupied molecular orbital (LUMO) of the outermost acceptor. The device consists of a subphthalocyanine (SubPc)/fullerene (C60) heterojunction that is modified by inserting one or two additional donor layers between SubPc and the anode. We find that two-, three- and four-layer structures yield similar Voc (1.0 V, 0.91 V and 0.94 V, respectively), even though the outermost HOMO-LUMO offset decreases from 1.4 eV to 1.10 eV, and to 0.9 eV, respectively. Analysis of the turn-on voltage in dark provides further evidence that open-circuit voltage is not limited by the outermost HOMO-LUMO offset.     

Optimization of equipment capacity and an operational method based on cost analysis of a fuel cell microgrid

A microgrid requires a stable supply of electric power and heat, which is achieved by the cooperative operation of two or more pieces of equipment. The equipment capacity and the operational method of the equipment were optimized using a newly developed orthogonal array-GA (genetic algorithm) hybrid method for an independent microgrid accompanied by a fuel cell cascade system, solar water electrolysis, battery, and heat storage. This type of system had not been hardly developed until now. The objective function of the proposed system was the minimization of the total amount of equipment and fuel cost over ten years. For the first step in the proposed analysis method, the capacity of each piece of equipment and the operational method, which are considered to be close to the optimal solution of the system, are combined using the orthogonal array and factorial-effect chart, which are an experimental design technique. In the next step, the combination described above provides the initial values to the GA, and the GA searches for the optimal capacity and operational method for each piece of equipment in question. Compared with a simple GA, the convergence characteristic improves greatly using the proposed analysis method developed in this study.     

Zircaloy-U02 and -Water Reactions and Cladding Temperature Estimation for Rapidly-Heated Fuel Rods under an RIA Condition

Zircaloy cladding chemical reactions with coolant water and UO₂ fuel at elevated temperatures under a reactivity initiated accident (RIA) condition were studied from a metallurgical point of view on the basis of the nuclear safety research reactor (NSRR) experiments. The cladding-fuel chemical reaction was extensively analyzed and found to be explainable from equilibrium phase diagrams. The systematic estimation methods of maximum cladding temperature were proposed and examined from metallographies. Maximum cladding temperature can be estimated from measured oxidation thicknesses in the temperature range of 1,000~1,600°C, from melting microstructures in the range of 1,600~1,950°C and also from the volume fraction of the precipitates, (U, Zr)0_2-x, in once-molten oxygen-stabilized α-zircaloy in 1,950~2,400°C. The estimation by the method proposed in the paper is more valid than thermocouple indications at high temperatures, since thermocouples perturb the temperatures they are measuring or fail at the extremely high temperatures. The results are thought to be applicable also to understand general fuel rod behavior under hypothetical accident conditions which cause severe fuel damage.