Rapid and Practical Impedance Measurement Technique for Health and Condition Monitoring of Solid-Oxide-Cells (SOC) Under Dynamic Operating Conditions
Bibliographic record
Abstract
Power-to-X (P2X) technologies have emerged as a crucial approach for converting surplus renewable electricity into storable and usable energy. One of the most promising applications of this concept is hydrogen production via electrolysis, where excess renewable energy is used to split water into hydrogen and oxygen [1-3]. Among the most efficient technologies enabling this conversion are high-temperature solid oxide cells (SOC)—versatile systems that operate in dual modes: as solid oxide electrolysis cells (SOEC) for hydrogen production and solid oxide fuel cells (SOFCs) for converting hydrogen back into electricity [4]. This bidirectional functionality makes SOCs particularly well-suited for integrating renewable energy into the power grid, providing an efficient solution for both energy storage and electricity generation. By 2030, SOECs are expected to lead the electrolysis industry due to their high electrical efficiency, lower material costs, and ability to function as both fuel cells and electrolysis cells. Despite the afore-mentioned advantages, SOC technology currently faces significant hurdles, one of which is the limited cell/stack lifetime of around 20,000 hours. This number also highly depends on the operating conditions and dynamic load changes which, if not managed properly, will result in a reduction of service life, and safety [5]. To avoid this, health and condition monitoring of the SOCs has proved to be highly powerful tool [6]. This is because it can be used to identify different stress and aging factors in the cell which can be used for better management of the operating conditions of the SOC. The health and condition monitoring can also be used to identify when the SOC system is reaching the end of life and should be changed/serviced before the performance of the system is considerably affected. Among the most widely adapted characterization and health monitoring methods is the electrochemical-impedance-spectroscopy (EIS) which, besides hydrogen technology, is widely applied technique for characterization of many other different energy storage technologies (e.g. batteries) [7-8]. The EIS is able to non-invasively reveal different characteristics of the SOC, including the stress and aging factors via the internal impedance which is essential information for the appropriate management of the SOC system operating in both fuel/electrolyzer cell modes. In practical applications, however, the conventional EIS is not the most feasible option due to long measurement time, and the fact that it applies sinusoidal signals which are difficult to generate with low-cost electronics and hardware [8-9]. Moreover, the EIS poorly tolerates any drifting of the operating conditions within the measurements which also hinders the practical applicability of the EIS. An attractive alternative is provided by the pseudo-random sequence (PRS) perturbation signals, that are able to produce rapid measurements, and which are comprised of only a few signal levels, facilitating their practical implementation in a real-world SOC stack/system [8]. In particular, a special three-level PRS perturbation has been recently validated to be highly effective for real-time monitoring of Li-ion battery impedance under drifting operating conditions [9]. Capability to perform at drifting operating conditions can be highly beneficial for the SOCs which often experience drifting operating conditions in practice [10]. This work demonstrated the use of three-level PRS perturbation for rapid and practical impedance measurements of SOC operating at fuel cell mode. The fuel cell operation is conducted at 700 °C, with a gas mixture of 1% H₂ and 20% N₂ on the fuel side and 20% air on the air side. Due to the limitations of the available laboratory equipment, only fuel cell mode was demonstrated in this study. During the measurements, the open-circuit-voltage (OCV) of the SOC has not yet stabilized after the system start-up which demonstrates an example of such dynamic (or non-steady-state) operating conditions. The three-level PRS perturbation is superimposed on a DC discharging current of 36 mA with the other two PRS signal levels corresponding 0 A and 72 mA. The measurement duration in total was two seconds and it covers a bandwidth of 2.5 Hz – 4 kHz. The results are presented in Figure 1, which shows both the measured Nyquist curve (left figure) along with the voltage and current partial data records (right figure). Small drifting of the OCV of the SOC indicates the dynamic operating conditions. Overall, the proposed method can rapidly (i.e. in two seconds) produce realistic impedance results which can be further applied to health assessment of SOC. FIGURE CAPTION: "Figure 1. Measured impedance spectrum (left). Voltage and current samples of the measurements (right)" The future work will focus on validating the PRS capability to perform also on electrolyzer mode. Moreover, the method performance under different realistic current profiles, such as those often found in electric vehicles, or in typical electrolyzer use-cases should be validated. An important part of the future studies is also to prove that the impedance data measured at dynamic operating conditions is valid and eventually applicable to health monitoring algorithms of SOC cell/stacks/systems. References: [1] - B. L. H. Nguyen, M. Panwar, R. Hovsapian, K. Nagasawa and T. V. Vu, "Power Converter Topologies for Electrolyzer Applications to Enable Electric Grid Services," IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, Canada, 2021, pp. 1-6. [2] - J. X. Jin, X. Y. Chen, L. Wen, S. C. Wang and Y. Xin, "Cryogenic Power Conversion for SMES Application in a Liquid Hydrogen Powered Fuel Cell Electric Vehicle," in IEEE Transactions on Applied Superconductivity, vol. 25, no. 1, pp. 1-11, Feb. 2015. [3] - L. Feng, Z. Zhang, X. Fu and X. Guo, "Analysis and Comparison of Partial Power Converters Based on Dual Active Bridge and Isolated Full Bridge Boost in Hydrogen Production System," 2023 IEEE 2nd International Power Electronics and Application Symposium (PEAS), Guangzhou, China, 2023, pp. 2532-2537. [4] - A. Hauch, R. Küngas, P. Blennow, A.B. Hansen, J.B. Hansen, B.V. Mathiesen and M.B. Mogensen, “Recent advances in solid oxide cell technology for electrolysis,” Science, vol. 370, p. eaba6118, Oct. 2020. [5] - Vanja Subotić, Bernhard Stoeckl, Vincent Lawlor, Johannes Strasser, Hartmuth Schroettner, Christoph Hochenauer, “Towards a practical tool for online monitoring of solid oxide fuel cell operation: An experimental study and application of advanced data analysis approaches”, Applied Energy, Volume 222, 2018, Pages 748-761. [6] - Xu, Y.; Shu, H.; Qin, H.; Wu, X.; Peng, J.; Jiang, C.; Xia, Z.; Wang, Y.; Li, X. “Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter”. Energies, 2022, 15 , 2534. [7] - Saeed Asghari, Ali Mokmeli, Mahrokh Samavati, “Study of PEM fuel cell performance by electrochemical impedance spectroscopy”, International Journal of Hydrogen Energy,Volume 35, Issue 17, 2010, pp. 9283-9290. [8] - Gjorgji Nusev, Bertrand Morel, Julie Mougin, Ðani Juričić, Pavle Boškoski, “Condition monitoring of solid oxide fuel cells by fast electrochemical impedance spectroscopy: A case example of detecting deficiencies in fuel supply”, Journal of Power Sources, Volume 489, 2021. [9] - Sihvo, J., and Stroe, D.-I. “Real-time impedance monitoring of li-ion batteries under dynamic operating conditions: The discrete Fourier transform eigenvector approach”, Cell Reports Physical Science, CellPress, Early access, 2025. [10] - Zewei Lyu, Hangyue Li, Minfang Han, Zaihong Sun, Kaihua Sun, “Performance degradation analysis of solid oxide fuel cells using dynamic electrochemical impedance spectroscopy, Journal of Power Sources, Volume 538, 2022. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".