Voltage Reduction Technique for Use With Electrochemical Impedance Spectroscopy in High-Voltage Fuel Cell and Battery Systems
Bibliographic record
Abstract
An attractive application of electrochemical impedance spectroscopy (EIS) is for diagnostic of a fuel cell (FC) or a battery system during operation. The use of EIS, however, is mostly limited to low-voltage (LV) FC systems and laboratory environments. Hence, the application of EIS in advanced diagnostics of a high-power system certainly lacks due to the voltage limitation and/or cost of the equipment. In this paper, a precision, low-cost electronic interface is proposed which enables the use of existing LV ac diagnostic tools with a production-size FC or battery stacks without the need for postprocessing of data. The interface, a dc level reducer (DLR), reduces only the dc component of the stack voltage to a safe voltage of <;60 without altering the ac diagnostic components. This paper explains in detail the development of the DLR circuitry. The scalability and real-world capability of the interface are demonstrated by developing it for two voltage ratings. A set of circuits rated for 30 V is tested with a nine-cell proton exchange membrane FC (PEMFC) stack, and circuits rated for 200 V are tested on 90and 110-cell commercial PEMFC stacks. The stack voltage is reduced by 60% on the nine-cell stack, and 60%-90% on the 90and 110-cell stacks. The accuracy is measured using EIS data for 74 frequency points in the range of 0.1-20 kHz, with and without the DLR. The maximum relative error for point-versus-point comparison of the impedance is measured at 0.8% and 1.4% for 30and 200-V rated circuits, respectively. These errors are well within the error of the industrial measurement equipment used, proving the fidelity of the ac signal output from the DLR.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".