Fuel Cell Ageing Prediction and Remaining Useful Life Forecasting
Bibliographic record
Abstract
Fuel cell (FC) lifetime prediction is becoming an integral part of any energy management strategy (EMS) in electrified vehicles since it is one of the key barriers in the commercialization of FCs. Remaining useful life (RuL) estimation is one of the most effective predictive maintenance tools used to develop EMSs. Prognostic health management (PHM) techniques can track the degradation of a FC and predict its RuL. Hence, in this paper, a data-based PHM method based on Long Short-Term Memory Network (LSTM) is proposed to predict the RuL of FC. This manuscript presents a benchmark to compare the prediction of the FC voltage degradation with other works that have used the same dataset. The results indicate an accuracy of 88.13% with RMSE of 0.0079, and RuL is forecasted for 135 hours of operation with an accuracy of 92.5%. Furthermore, LSTM is used to predict ageing trend of FC by considering a different current profile that the network has been trained with. This generalization has an accuracy of 79.99%. with an RMSE of 0.0351.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".