SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Presentation given by Harry Abernathy reviewing FY23 and FY24 results from the NETL SOFC Federal Work Plan. The presentation is from the Spring 2024 FECM Project Review Meeting in Pittsburgh, PA on April 25, 2024.
Analysis of the total energy of an operating SOFC or SOEC.
National Energy Technology Laboratory (NETL) provides system-level process, cost, and market analyses on solid oxide cell (SOC) based technologies. Specifically, techno-economic analyses (TEA), market assessments, and other technology evaluations serve to guide the U.S. Department of Energy (DOE) Office of Fossil Energy and Carbon Management (FECM) Reversible Solid Oxide Fuel Cell (R-SOFC) Program technology goals and objectives. These studies are key to describing how the technologies contribute to improving domestic energy infrastructure in a clean, efficient manner. This effort seeks to elucidate the potential integration opportunities between reversible SOCs and industrial systems which would aid in SOC commercialization and deployment. These preliminary screening-level results show what opportunities exist for power generating SOFCs, hydrogen producing SOECs, and point-source carbon capture. Improvements can be seen through changes in cost of electricity, cost of hydrogen, and cost of carbon capture.
This program was designed to analyze the 3-phase microstructure of the electrodes of a solid oxide fuel cell (SOFC) or electrolysis cell (SOEC), both referred to in combination as a solid oxide cell (SOC). It is agnostic to the exact system, so it could be repurposed to analyze any 3-phase microstructure. This tool directly analyzes segmented voxel-based data that has been segmented into phase IDs (1,2,3). The voxels will be analyzed directly for: - tortuosity factors - triple phase boundaries - 2-phase interfacial areas, using a meshed isosurface - mean diameters of each phase, using an inscribed sphere method - standard deviation of the diameters of each phase, from the same inscribed sphere data - connectivity information Comprehensive information is available in the readme file (within the zipped repository in Markdown language, and also available here as a rendered PDF). Please cite this page / DOI, as well as https://doi.org/10.1111/jace.14775, for usage.
The project objective was to demonstrate a solid oxide cell-based steam electrolysis stack that exhibits robustness, reliability, endurance, hydrogen purity, and produces hydrogen at elevated pressure of 2 to 3 bar. Innovative materials and processing methods were evaluated to improve degradation characteristics. Performance improvement focused on nearly all layers involved in the cell and stack assembly. Primary attention was paid to zirconia-ceria interface resistance control via sintering optimization and decrease in degradation from the oxygen electrode by evaluating low strontium (Sr) or Sr-free composition for both the oxygen electrode and current collection layer. Stack robustness was addressed by validating redox tolerance of fuel electrode, confirming capability of cells to survive repeated thermal cycles, studying the effect of pressure on performance and degradation, evaluating the effect of contamination on fuel and oxygen electrode performance and degradation, and identifying mitigation strategies to improve performance. The characterization included evaluation of electrochemical performance and stability followed by microstructural analysis. At the cell level, performance and stability improvements were achieved by incorporating a Sr-free oxygen electrode and a denser oxygen electrode barrier layer. At the stack level, pressurized operation reduces demand on first stage compression, the redox tolerant fuel electrode mitigates risk from service interruptions, and improvements to interconnect coating alleviate chromium (Cr) contamination effects. The denser barrier layer was achieved by adding a sintering aid to the samaria-doped ceria (SDC) composition that reduced sintering temperature by 150 °C. The resulting density was on par with the baseline SDC barrier layer density and the lower sintering temperature resulted in less resistive phase formation during sintering. Button cell tests did not demonstrate a change in performance when exposed to silicon (Si) or manganese (Mn) impurities to the fuel electrode and Cr impurity to the oxygen electrode. More detailed study however is warranted. The project addressed SOEC performance and stability at the cell and stack levels through a systematic approach to known sources of degradation that were combined and tested in three stack tests using an electrolyte supported cell design to allow for evaluation of a variety of fuel and oxygen electrode compositions. STK-82 and STK-83 had identical compositions. STK-100 incorporated the best materials and processing variables developed under this and concurrent projects, and was tested at elevated pressure in steam electrolysis. • STK-82 recovered performance after redox and thermal cycling, demonstrating the robustness of the stack and seals. It exhibited stable performance in testing for 500 hours in SOEC mode, followed by 300 hours of cycling between SOEC and SOFC tests. Degradation during SOEC operation was 1.8 %/ 1,000 hours. • STK-83 generated hydrogen at >80% steam conversion, and oxygen above 98.5 % purity during pressurized operation. Both hydrogen and oxygen were generated at 3 barg pressure without the use of a pressure vessel. In addition to balanced pressure, electrolysis operation at 1 bar differential pressure across anode and cathode was also demonstrated to substantial the robustness of the cell and seal. • STK-100 measured at initial ambient pressure conditions showed an area specific resistance of 1.1 ohm-cm 2 , and STK-83 had 1.3 ohm-cm 2 .
The operating lifetime of Ni-YSZ fuel electrodes used in solid oxide electrolysis cells and fuel cells (SOECs and SOFCs) is limited by Ni redistribution, one of the primary degradation mechanisms that must be overcome to extend the longevity and maximize the performance of SOECs and SOFCs. To achieve this, 3D microstructural data is needed to relate both initial performance and performance loss over time to microstructural properties and their evolution throughout operation under various conditions. However, 3D microstructure data remains relatively scarce within the literature due to multiple challenges in acquiring and analyzing such data reliably. This work presents a workflow for acquiring and processing synchrotron X-ray nanoscale computed tomography (nano-CT) data from Ni-YSZ electrodes. Parameters for each step in the nano-CT workflow are described up to the final result (a 3D reconstruction), with particular emphasis on image alignment using freely available software. Following the results of a parametric sweep of the image alignment step, high contrast, low signal-to-noise 3D nano-CT data is obtained with relatively short compute times. While the exact methods best suited to samples with different microstructural qualities, or similar Ni-YSZ nano-CT data obtained from other sources may deviate from the solution found herein, this work also generalizes the decision points and evaluation of each step to provide a starting point to adapt this workflow to other datasets.
Here, this study evaluates the durability of Ni/YSZ-supported SOECs under dynamic operating conditions relevant to real-world applications. Systematic tests were conducted to assess cell performance under steam cycling (3 to 75% humidified H 2 ), mode cycling between SOFC and SOEC operation, thermal cycling (150 to 750 °C at OCV, and 600 to 800 °C at 1.3V), and redox cycling (between 50% humidified H 2 and 50% humidified N 2 ). Steam cycling, mode cycling, and thermal cycling at OCV do not significantly accelerate performance degradation. Thermal cycling at 1.3V caused minimal damage within 600 to 800 °C. Full redox cycling (multi-hour oxidation holds) induced cell structural failure, while partial redox cycling (0.5 h holds) was tolerated. Extensive characterization revealed some material evolution, namely Sr and Co secondary phase formation, within the oxygen electrode due to La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ instability, especially for steam cycling and mode cycling. Minimal changes were identified within the Ni-YSZ fuel electrodes. These findings provide critical insights into SOEC reliability under dynamic conditions, supporting their application in dynamic or intermittent energy systems.
Overview -------------------------- This repository contains datasets from the manuscript **"Enhanced Generalizability to Deep-Learning Quantification of 3D Microstructural Characteristics through Microstructurally Aware Augmentation of Scarce Data"** (*William F. Kent, Rochan Bajpai, Rachel C. Kurchin, William K. Epting, Harry W. Abernathy, Paul A. Salvador. Submitted 2026*). The methods are also described in the dissertation **Data Intensive Analysis of Solid Oxide Cell Microstructures** (*Doctoral dissertation, Carnegie Mellon University, 2025*). The datasets here are trained convolutional neural network (CNN) models for predicting key microstructural properties of solid oxide cell (SOC) electrodes from low-res, 2-channel 3D images, as well as some helpful code. The parameters for input images are provided in the paper. Sample data is provided in the file `Combined_anode_aug_dual_1k_examples` - that particular data was used to train `anode_all_aug.pth` and will work most accurately with that model. Please familiarize yourself with all caveats on accuracy and applicability, as detailed in the associated paper. Usage -------------------------- The basic usage is as follows, assuming `model_fn` is the path to the .pth file, and `X` is 2-channel input image(s) of the proper dimensions (either one image of shape `[2,12,24,24]`, or a batch of N input images of shape `[N,2,12,24,24]`): from CNN_inferencer import load_model_for_inference model = load_model_for_inference(model_fn) y_predicted = model(X) The model object automatically handles input scaling and output de-scaling based on the way the models were trained - in other words, pass in a 2-channel micro-CT image, and it will output microstructural property values in real units. ## Other model object attributes Note that model has useful attributes other than its forward pass model(X). * `model.output_descaler` - returns the output descaler object. Model does the de-scaling when generating inferences, but you may want to re-use this de-scaler on other values to e.g. compare predictions to ground truth from already-scaled training data. * `model.prop_names` - Gives the property names of the predicted y values, in order. Only exists if there's an output scaler as part of the model object, which there will be in the models provided here. ## Usage with sample data Here is a short script to use with the included sample data. from CNN_inferencer import display_predictions, load_model_for_inference, calculate_mape, parity_plot import h5py import numpy as np model_fn = 'anode_all_aug.pth' data_fn = 'Combined_anode_aug_dual_1k_examples.h5' N_samples = 200 figure_outdir = '.' model = load_model_for_inference(model_fn) with h5py.File(data_fn,'r') as f: XX = f['X'] #These are the 2-channel 3D images yy = f['y'] #These are the ground-truth microstructural properties, but they have been scaled for training - need to de-scale below N = XX.shape[0] #How many images total in the input data file #Run inferences on N_samples random samples from XX. #Run in a batch, much more efficient than one at a time. ii = np.random.choice(N,N_samples,replace=False) ii.sort() y_pred = model(XX[ii]) #Get the original/true (but normalized/scaled) values from the training dataset... #Because they were normalized, they are not in real units yet. So let's also de-scale them using model.output_scaler. y_true = model.output_scaler.transform(yy[ii]) #Let's display actual values for just 5 random ones for i in np.random.choice(N_samples,5,replace=False): display_predictions(y_true[i], y_pred[i], model.prop_names) #Make parity plots for each property (ground truth vs predicted values) #Also label each plot with the mean abs. percent error (MAPE) of the predicted values for i,key in enumerate(model.prop_names): mape = calculate_mape(y_true[:,i], y_pred[:,i]) parity_plot(y_true[:,i], y_pred[:,i], figure_outdir, key, extra_title=f' ({mape:.2f}% MAPE)')
Abstract Solid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation.
This pre-trained ML model is a tool that uses basic compositional parameters for porous solid oxide cell (SOC) electrodes - the phase fractions and mean particle/pore diameters – as inputs and uses them to estimate additional electrochemical performance parameters: active (i.e., connected) TPB density, all tortuosity factors, and phase pair specific interfacial areas. The electrode is assumed to be composed of two solid phases and a pore phase. The property calculations are performed using neural network regression models trained on a large bank of synthetic electrode microstructural data that NETL has generated using the program DREAM3D (that bank is also hosted on EDX: https://edx.netl.doe.gov/dataset/soc-synthetic-microstructure-bank). This means the generated parameters are based on training from actual measured properties from 3D microstructures, not estimated from geometric simplifications. This tool was developed and is intended to replace percolation theory calculations in models that use hypothetical electrode properties. An example use case would be running SOC performance simulations across a parametric sweep of electrode designs (e.g., varying phase fractions and particle sizes) and assessing how it impacts the electrochemical performance of the SOC. Within the parameter space of the training data (statistics of that parameter space is provided in the readme file), this model achieves sub-5% mean absolute percent errors, an order of magnitude less error than percolation theory across the same parameter space. However, be aware that this tool was developed with parametric simulations in mind, and users are encouraged to assess accuracy for their own specific use case rather than taking accuracy metrics at face value. More info, including a usage guide, is in the included readme file. This tool should be cited with the DOI number provided.
Explore the source record for details and available documents.
Solid oxide fuel cells (SOFC) and solid oxide electrolysis cells (SOEC) have received great interest due to their highly effective reversibility as power generation and H2 production system without releasing any greenhouse gases into environment. The LSCF electrode exhibits a higher structural and performance stability under both SOFC and SOEC operation due to its mixed ionic and electronic conductivity, and there is no immediate delamination taking place during the initial several hundred hours operation. However, the LSCF based air electrode still presents significant performance degradation (with the increased resistance) over the prolonged operation, such as over 1000 hours of operation under SOFC and SOEC. The influence factors for the cell’s performance and stability need to be optimized to improve the power generation for SOFC and H2 production for SOEC. The effects of operational temperature on the performance and durability for both SOFC and SOEC are electrochemical operation dependent. The performance and performance durability for the first 1500h were currently studied under optimized operational temperature for reversible SOFC/SOEC.
Symmetric-structure metal-supported solid oxide fuel cells and electrolysis cells (MS-SOFCs, MS-SOECs) offer several advantages over conventional solid oxide cells, including the use of inexpensive materials, high mechanical strength, and rapid ramp-up ability. Aggressive operation of MS-SOCs in fuel cell mode is well-established, including extremely fast start-up, redox tolerance, and imbalanced pressure. Here, we extend dynamic operation to MS-SOCs in SOEC mode with high steam content for both small button cells and a large rectangular cell, including: steam cycling, thermal cycling, redox cycling and power cycling. Steam cycling entailed switching between 3:97 and 50:50 steam:hydrogen ratio. For thermal cycling, the temperature was rapidly varied between 150°C and 700°C for 50 cycles. Redox cycling involved switching the steam side gas between 50 % humidified H 2 and 50 % humidified N 2 for 5 cycles. Power cycling was performed by operating the cell under variable current density, resulting in cell voltage between 1.3V and 2.8V. Degradation rates for each testing strategy were compared to a baseline cell, and found to be similar. In conclusion, the excellent tolerance to dynamic operation increases confidence that MS-SOECs will be compatible with dynamic or intermittent renewable resources.
Reversible solid oxide cells(rSOCs) are an enabling energy storage/production technology for a dynamic grid environment. Reversible operation requires strong working knowledge of fuel cell (SOFC) and electrolyzer (SOEC). Performance degradation of SOECs has been observed; however, the details of physical processes related to the performance degradation remain unknown. Multiphysics simulations were performed to investigate the performance degradation of solid oxide electrolysis cells under various working conditions.
Solid oxide electrolysis cells (SOECs) for H 2 production is a core technology for H2@scale. However, its conventional manufacturing methods adapted from the manufacture of solid oxide fuel cells (SOFC) need high cost, especially for small-volume production. The emerging laser 3D printing (L3DP) technology with computer-aided 3D printing and computer-controlled laser processing can fulfill layer-by-layer digital shaping and rapid in-situ consolidating feedstock into complicated geometries. L3DP, a promising additive manufacturing (AM) technology, has achieved significant success in manufacturing plastic and metal parts, which is currently attracting considerable attention for the cost-effective, rapid, and flexible manufacturing of heterogeneous multilayered ceramic devices (e.g., SOECs, SOFCs, and solid-state batteries). It is believed that the L3DP can integrate the advantages of selective consolidation of heterogeneous layers, accurate control of layer microstructures, high processing heat efficiency, high processing speed, high stack compactness, high stack design flexibility, and low stack sealing area for cost-effective, rapid, and flexible manufacturing of SOECs to meet DOE’s electrolyzer target.
This report evaluates the cost and performance of several types of Integrated Energy Systems (IES) based on solid oxide fuel cells (SOFCs) to generate power and solid oxide electrolysis cells (SOECs) to produce hydrogen. All cases feature carbon capture at rates exceeding 97 percent. The report also describes the development of optimized steady state process models for each system. These are used to calculate overall electricity and hydrogen production costs using a consistent methodology that facilitates comparisons of these cases to one another and to prior NETL cost and performance estimates. The SOFC and SOEC costs and performance are based on nth-of-a-kind systems and include research and development and learning associated with mass-scale commercial deployment of the technology over the next decade required for commercial utility-scale systems.
This is the Journal of the Electrochemical Society manuscript that details an NETL analysis on comparing the performance and cost of reversible solid oxide cell (SOC) units versus paired fuel cell (SOFC) and electrolysis cell (SOEC) units. The analysis compares tradeoffs such as capital costs versus rate of performance degradation, sensitivity to natural gas prices, sensitivity to imported power costs, and other relevant analyses. The systems include a high rate (98%) of carbon capture. The analysis shows that given a certain cost of electricity input, either approach could be viable in the market and points to the need to incorporate into a full system to understand impacts.
A simple, fast, and economical alcohol penetration method for assessing the solid oxide cell to metal window frame seal in a typical planar design is presented. An alcohol such as ethanol or isopropanol is placed into the cavity of a cell sealed to the window frame. Within 3–5 min, one can determine if the glass seal is hermetic by visual observation along the seal edges on the side of the sealed frame. Cross bubbling and open circuit voltage methods for determining whether the seal failed or cracked at high temperature after final stack firing are also discussed.