Recycling of Solid Oxide Cell Stacks – Ideas for the Future

To better understand degradation in electrochemical converters and helping to correlate certain phenomena with specific operating conditions, machine learning (ML) methods are increasingly being applied. Success has already been achieved in the field of degradation analysis and prediction of capacity of lithium ion batteries1, for instance. In terms of Solid Oxide Cell (SOC) stacks ML methods have been applied mainly with the aim of identification of faulty operation modes and degradation related fault diagnosis2. ML approaches usually require a considerable amount of real training data, when used for forecasting models. A data consolidation and curation strategy was developed with the aim of processing the historic long-term test bench data of SOCs collected by Forschungszentrum Jülich over the past years. In comparison to other datasets developed in this field3, the one presented in this work contains SOC stack tests in fuel cell operation with significantly longer operating times under load. A compilation of the sample experiments and the consolidation into a hierarchical data format are presented. Further, an essential part of the strategy is the automatic curation and analysis of electrochemical impedance spectroscopy (EIS) measurements, using a specifically developed procedure in Python. The varying quality of measurements from past years, as well as recurring artefacts such as parasitic inductances, can be addressed in this way. Additional distribution of relaxation times (DRT) deconvolutions and equivalent circuit modelling (ECM) are performed, as part of the procedure to automatically retrieve feature values from measurements (cf. Fig. 1). The novel dataset, which to the authors’ knowledge includes some of the longest SOC stack tests available, serves as the basis for several evaluations. In addition to classification and clustering work to derive degradation patterns, in particular based on the EIS data, another focus is on the development of forecasting models. The current work is primarily concerned with long short-term memory (LSTM), as well as regression models that make use of both the time series data and the characterisation measurements, such as EIS. Acknowledgement The authors would like to thank their colleagues at Forschungszentrum Jülich GmbH for their great support and the Helmholtz Society as well as the German Federal Ministry of Education and Research for financing these activities as part of the WirLebenSOFC project (03SF0622B). References 1: Jones, P.K., Stimming, U. & Lee, A.A. Impedance-based forecasting of lithium-ion battery performance amid uneven usage. Nature Communications 13, 4806 (2022).2: B. Yang et al. Solid oxide fuel cell systems fault diagnosis: Critical summarization, classification, and perspectives. Journal of Energy Storage 34, 102153 (2021).3: A.K. Padinjarethil, S. Pollok & A. Hagen. Degradation studies using machine learning on novel solid oxide cell database. Fuel Cells 21, 566–576 (2021). Figure caption: Fig.1: Flow diagram of EIS data curation pipeline and curation results for example EIS measurement. Figure 1

Increasing demand for long-term energy storage, reduced carbon emissions, more resilient grid, and the lowest cost grid balancing solutions promotes solid oxide electrolysis cell (SOEC) and reversible solid oxide cell (RSOC) technologies. These systems can achieve high hydrogen production rates with near 100 percent stack electrical efficiency while co-electrolysis of carbon dioxide and water leads to production of syngas. RSOC can serve as a grid storage device (hydrogen production) and power production plant (provide electricity during peak demand). With over 25 years’ experience building and testing solid oxide cell materials, components and stacks, Nexceris has a proven track-record of commercializing products to major OEMs. The established manufacturing platforms at Nexceris enable smooth expansion into new applications.In this talk, development of cells, stack components and stacks will be discussed. Performance and durability improvements from single cell to short stacks will be addressed. Cells with downselected electrode sets were implemented into short stacks with active cell area of 42 cm2 and 96 cm2. Stacks were operated continuously in SOEC mode at ~1.3 V for ≥ 500 hours with <5%/kh degradation and improving. The resulting short stack demonstrations show promising baseline performance and durability platform for future cell and stack development. RSOC tests were recently initiated with dynamic operation. Nexceris’ ChromLok coatings for bipolar plates were also integrated to stacks to enhance durability.

Abstract

Stress development in metallic interconnects during operational cycles of solid oxide cells (SOCs) could create permanent plastic deformations, which can cause contact losses in the interface between the interconnect and cells. The magnitude of these stresses and their distribution within the SOC stack are dependent on the overall design of the stack and its operational parameters/conditions. In this study, analysis of stress generation in metallic interconnects due to temperature and stack assembly loadings for different types of generic SOC stack designs operated under the same operational cycles is performed. The analysis includes stack design concepts with and without contact layers having cross-shaped and/or corrugated metallic interconnect. To consider the effect of thermal gradient over the surface of the stacks (due to electro-chemical reaction), stacks with two types of temperature profiles are studied. The analysis is performed numerically using finite element modeling of a standard repeating unit of SOC stack. Special focus is given to stresses that can cause possible delamination of the interconnect from the cell that subsequently leads to loss of electrical contact. Comparison of stress for different types of stack design concepts are presented. An effective way to minimize stresses in the interconnect is found to be through designing the micro-architectures of the interconnect and us eof contact layers materials. Key words: Solid oxide fuel cells, Metalic interconnect, Creep, finite element modeling

Advancing synthetic fuel technology: A model study for the integration of direct air carbon capture and diesel synthesis

The OxEon Energy team continues its 30+ year solid oxide fuel cell (SOFC) development history with the design, fabrication, and installation of two reversible solid oxide electrolysis (SOEC)/SOFC demonstration modules scheduled for installation and commissioning in 2023. The high temperature electrolysis (HTE) systems produce hydrogen through electrolysis using solid oxide cell (SOC) technology derived from OxEon’s heritage stack technology and advancements made during stack development for NASA’s Mars2020 mission. The demonstration units integrate reversible SOC stacks with an effective and reliable balance of plant (BOP) system. A 4-stack quad assembly forms the basis for a modular, scalable system. Thermal management includes pre-heaters within the hot section unit (HSU) enclosure and a feed path that takes advantage of the exotherm generated in SOFC mode. The system design applies mechanical compression to the stacks outside the HSU enclosure to minimize insulation envelope size and produce greater force on the stacks.

Solid oxide electrolysis cells (SOECs) and solid oxide fuel cells (SOFCs) are the leading high-temperature devices to realize the global “Hydrogen Economy”. These devices are inherently multi-material (ceramic and cermets). They have multi-scale, multilayer configurations (a few microns to hundreds of microns) and different morphology (porosity and densification) requirements for each layer. Adjacent layers should exhibit chemical and thermal compatibility and high-temperature mechanical stability. Added to that is the need to stack many cells to produce reasonable power. The most critical barriers to widespread global adoption of these devices have been their high cost and issues with their reliability and durability. Given their complex structure and stringent requirements, additive manufacturing (AM) has been proposed as a possible technological path to enable the low-cost production of durable devices to achieve economies of scale. However, currently, there is no single AM technology capable of 3D printing these devices at the complete cell level or, even more difficult, at the stack level. This article provides an overview of challenges that must be overcome for AM to be a viable path for the manufacturing of SOECs and SOFCs. A list of recommendations is provided to facilitate such efforts.

Solid oxide cells can deliver highly efficient energy conversions between electricity and fuels/chemicals. A central challenge of upscaling solid oxide cells is the probability of failure of the brittle ceramic components. The failures of the ceramic components may lead to significant degradation or eventual failure of a stack. To predict mechanical failures in a stack, a full stack model is needed, together with a local assessment of stresses at the vicinity of failing regions, e.g. the contact points between the cells and interconnects. A conventional three-dimensional model requires a very fine discretization of the mesh to capture stress intensities. Computational resources needed for such a model are therefore immense, and it is highly unlikely to compute at stack scale, as well describe the evolution over time. In this work, the homogenization modelling framework for solid oxide cell stacks is extended to identify local mechanical failures. Thus, the fracturing within a local failing point is examined by using a localization approach, where stresses in the stack model are linked to the local stresses and the energy release rate at the crack tip of the relevant interface. This is done in a general manner, such that the local stresses and the energy release rate can be evaluated at every point in the stack at every instant of time without loss of computational efficiency. A 100-cell stack can be modelled in three dimensions with all coupled multiphysics in steady state within 3 min on a current workstation computer.

Ceramic ionic conductors are applied in innovative systems for highly efficient and clean power generation. The oxygen separation membranes and solid oxide fuel cells (SOFC) and solid oxide electrolyzers (SOE) can be the examples.Influence of various structural properties of ceramic ionic conductors which are used for solid oxide fuel cells was well-defined and described using several mathematical models. Such parameters as ionic and electronic conductivity, porosity, tortuosity, shape and distribution of pores were included. In case of solid oxide electrolysis cells, effects of these parameters on the electrolysis reaction remain unclear and obscure. There is no clearly defined correlation, neither there is an optimization tool for modification of the parameters to maximize the performance of solid oxide cell operating in regenerative mode. For that reason it is necessary to perform a complete comprehensive analysis combining both the experimental and numerical techniques to identify the key microstructural parameters and their correlation with the performance of electrochemical reaction. The work was aimed at verification of data presented in earlier studies which advised that porosity of electrodes of SOEs needs to be different than in the case of SOFCs, however different values were claimed. Mingyi et al. [1] indicated that optimum porosity for SOEC is about 45%, however several other studies advised on different values.This paper presents results of numerical simulations and correlation of material properties with operational characteristic of a solid oxide electrochemical cell which were followed by initial experimental investigations towards validation of the numerical model, including the analysis of selected operational states. Model was established on the basis of theoretical correlations between microstructural parameters and macro-level performance. It was used for performing variant analysis of different modes of operation under varied conditions. The outcomes of the numerical investigations made it possible to define the requirements for fabrication of modified cells for high temperature electrolysis which were studied.The work presents preliminary results of electrochemical characterization of modified cells and an attempt to generalize the model of SOC customized to highly efficient electrolysis.The general concept of optimizing the performance of SOE by fine-tuning of the microstructure which was proposed earlier [2] is assessed throughout experiments using in-house 50 mm x 50 mm cells fabricated in the Institute of Power Engineering. The proposed path for tailoring the microstructural properties is presented and discussed.

The integration of Hydrogen technologies in different end-uses such as transport, electric microgrids, residential and industrial applications, will increase exponentially soon. Hydrogen as energy carrier allows more favourable energy conversion than other conventional systems and is crucial in worldwide decarbonize end uses. The production of green hydrogen, using RES, is a key area for the evolution of this technology. In this context, SWITCH is a Horizon 2020 European Project that aims to design, build and test an in-situ fully integrated and continuous multisource hydrogen production system, based on solid oxide cell technology. Reversible Solid Oxide Cell (rSOCs) technologies allow to convert renewable energy as hydrogen in the power-to-gas application (P2G) and in reversible mode is able to produce electricity from hydrogen stored, power-to-power application (P2P). rSOCs are really interesting to stabilize the random nature of RES because a combined electrolysis and fuel cell system should be able to switch between the two modes as quickly as possible in order to optimize the integration and the use of RES. However, rSOCs need a complex BoP from the thermal point of view, able to guarantee high efficiency even at partial load mode as well as easy start-up and shutdown procedures. In this work, a Stack Box Module dynamic model was developed in Modelica environment as a dynamic tool for the definition and optimization of BoP requirements. Stack model was validated in SOFC (Solid Oxide Fuel Cell) and SOE (Solid Oxide Electrolyser). The results of the simulation provide verification of the technical/thermodynamic behaviour and flexibility of a stack box of 70 cells. Dynamic modelling allows to evaluate the effect of the reagent inlet temperatures on the operation and hydrogen production/consumption in terms of yield as well as the transients between the different operative modes. Model has been validated by experimental measurements performed in the laboratory. In particular, the kinetics of the reactions governing steam methane reforming (SMR) was considered from data found in the literature, while the ASR (Area Specific Resistance) value was calibrated according to experimental data. The results of the dynamic model show as model can be a useful design and optimization tool for the SOCs technology.

A novel system for the production of pure hydrogen from natural gas based on solid oxide fuel cell–solid oxide electrolyzer

No abstract.

Production of syngas by solid oxide electrolysis: A case study

As renewable energy sources are inherently intermittent, their increasing contributions to electricity grids will necessitate large-scale energy storage to balance supply and demand. Storing electrical energy in chemical bonds by electrolysis of CO2 and / or H2O is one option to achieve this. We chose solid oxide reactors for this application, inter alia, because in principle they can be operated cyclically in electrolyser (SOE) and fuel cell (SOFC) modes. Results will be presented of measurements of ohmic and electrode polarization resistances and global performances for CO2 splitting to CO and O2 in micro-tubular solid oxide electrolysers (SOEs), with different types of current collectors; electrolysis of steam and (CO2 + steam) exhibited similar behaviour. Silver or nickel wire current collectors, with and without silver or nickel paste, were used to contact the 220 μm thick inner lumen Ni-YSZ electrode (SOE cathode) of micro-tubular solid oxide electrolysers [1] (SOEs: Ni-YSZ|YSZ|YSZ-LSM|LSM) incorporating 34 μm thick yttria-stabilized zirconia (YSZ) electrolyte, and 40 μm thick lanthanum strontium manganite (LSM, SOE anode), as described previously for CO2 reduction [2]. Silver wire and paste was used as the current collector for the LSM electrode. As shown in Fig. 1, the best performance (ca. 1 A cm-2 at 1.5 V (thermo-neutral potential difference) at 800 °C) was achieved for reactors with nickel wire current collectors alone, implying metallic pastes had no net beneficial effects on area-specific ohmic resistances. Furthermore, applying silver paste had a detrimental effect on the Ni cathode polarization resistance, for reasons that will be presented. Fig. 2 shows polarization and ohmic resistance data at open circuit; similar behaviour was exhibited with potential differences applied to reactors. Ohmic resistances were expected to decrease by using silver wire, due to its higher electronic conductivity compared to nickel, but no stable cell performance was achieved.

This work describes a recently-developed numerical model for three-dimensional, steady-state simulations of reversible solid oxide cell (rSOC) stacks, taking into account a heterogeneous temperature field. The model employs a volume-averaged approach, also referred to as the distributed resistance analogy. It considers fluid flow, multi-component species diffusion, as well as heat and mass transfer, including thermal radiation and electrochemical reactions. The implementation of the computational model is based on an open-source library, OpenFOAM. An in-house designed rSOC stack, Mark-H is considered. Simulations are performed for repeating units with a 320 cm2 active area, with both the present stack model and a one-dimensional Simulink model. Both models predict very similar voltages, with a maximum difference of 2% compared to experimental results. The present model shows a temperature distribution closer to the experimental data than the Simulink model, although a slightly longer simulation time is required.