Commercial supercapacitor parameter estimation from step voltage excitation
Bibliographic record
Abstract
Summary Supercapacitors are crucial elements in advanced industrial electronic systems particularly when supplied from renewable energy sources. Here, we derive expressions for the current, power, and stored energy in a supercapacitor excited with a step voltage signal. Although, it is not common practice to charge supercapacitors using a step voltage, these devices are sometimes used in switching‐type applications where they are subject to this type of signal. We validate the derived mathematical expression of the current via experiments on four different commercial devices. By fitting the measured current data to the derived expression, the parameters of these devices, which are modeled as a series connection of an internal resistance and a constant phase element (CPE), are also estimated. Power and energy are then evaluated followed by the estimation of the effective capacitance for each device. Our analysis makes use of fractional order calculus as opposed to classical integer‐order models.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".