State-of-health forecasting of solid oxide fuel cells using physics-informed temporal graph convolutional network
Bibliographic record
Abstract
Solid oxide fuel cells (SOFCs) are electrochemical devices that convert the chemical energy of fuel directly into electricity, offering a promising clean energy alternative to combustion-based power generation. However, SOFC degradation remains a major barrier to commercialization, highlighting the need for accurate and interpretable health monitoring tools. This study develops a real-time state-of-health (SOH) forecasting model for SOFCs under Redox cycling using a physics-informed temporal graph convolutional network (TGCN) that integrates voltage and electrochemical impedance spectroscopy (EIS) data. Physically meaningful impedance features are extracted through distribution of relaxation times (DRT) analysis, enabling the network to capture electrochemical process interactions governing degradation. The proposed model achieves a root mean square error (RMSE) of 0.085, corresponding to a 37% reduction in forecasting error relative to a baseline long short-term memory (LSTM) model that excludes impedance information. It also yields consistent improvements across mean absolute error (MAE) and mean absolute percentage error (MAPE), with the coefficient of determination ( R 2 ) reaching 0.94. Trained and validated on eight Redox datasets totaling over 2.1 million samples, the model demonstrates strong generalization and real-time feasibility, with an inference latency below 3 ms. This framework enables interpretable and accurate degradation forecasting, supporting predictive maintenance and extending cell lifespan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".