Method for Systematic Validation of a Physically Based PEMFC Model By Spatially Resolved Impedance Measurements
Bibliographic record
Abstract
Loss mechanisms in PEM Fuel Cells related to charge transfer reactions or diffusive gas transport result in a strongly nonlinear performance, which is furthermore affected by operating conditions as temperature, relative humidity of the gases and stoichiometries. These dependencies have to be considered and validated in fuel cell models to ensure accuracy. Thus, the interpretation of the simulated results becomes more reliable. Direct comparison of simulated and measured current/voltage-relation only allows to evaluate deviations in resulting cell voltages but exclude internal state variables of the model such as overvoltages due to different loss mechanisms. In consequence, simulated and measured values of voltage or current can concur by unnoticed compensation of different errors in magnitude and sign. Additionally, limited numbers of measurements and comparisons increase the probability and impact of this effect. Aggravating, every single process within the cell depends on the comprehensive combination of all operating conditions and runs simultaneously with all other ones. A direct comparison of individual simulated and measured polarization curves therefore is not a suitable and sufficient validation. We address this challenge by applying measurements of electrochemical impedance spectroscopy (EIS) during systematically varied operating conditions spatially resolved along the gas flow direction [1]. The loss processes within the cell can be separated by distribution of relaxation times (DRT) and quantified by a physico-chemical meaningful transmission line model. The resulting amount and distribution of resistances caused by loss processes is compared with the corresponding simulated values of a multiphysical model for observation and monitoring during operation [2]. These insights support the investigation of deviations between simulated and measured polarization curves and therefore the validation the model. In this contribution, the resulting physical interpretation is discussed. Consequences and conclusions for the further development of the cell model are demonstrated. [1]: P. Oppek et al., „ Spatially Resolved Deconvolution of Loss Processes in PEM Fuel Cells”, 241st ECS Meeting, Vancouver [2] T. Goosmann et al. „Impedance-Based, Multi-physical DC-Performance-Model for a PEMFC Stack”, 241st ECS Meeting, Vancouver
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".