Converter-Based Electrochemical Impedance Spectroscopy for High-Power Fuel Cell Stacks With Resonant Controllers
Bibliographic record
Abstract
Impedance spectrum is a key signature of a fuel cell stack (FCS). The variations of the impedance spectrum reflect the internal status of an FCS. Enabling electrochemical impedance spectroscopy (EIS) with the onboard power conditioning converter (PCC) provides an attractive approach for in situ diagnostics and condition monitoring of an FCS in end applications, such as heavy-duty vehicles. Although a few previous attempts were made with the PCC being controlled as the source of ac perturbations, the issue of how to properly produce a wide frequency range of perturbations to a high-power FCS has not been well recognized and addressed. In particular, the high-frequency portion is limited by the converter switching frequency and controller bandwidth. Different from existing approaches that fall short of being practical solutions for a high-power FCS, this article proposes the use of PI plus resonant controllers for the PCC to generate quality high-frequency perturbations without any additional hardware. Enabled by the proposed EIS method, an example application showing the effective detection of the impedance changes of an emulated FCS is also presented. The design and implementation of the scheme and the considerations of response measurement and impedance calculation are given in detail. Experimental verifications on a scaled-down laboratory setup demonstrate the validity and possibility of the proposed methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.001 |
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".