A Novel Parameter Estimation Scheme for Supercapacitors
Bibliographic record
Abstract
As reliance on sustainable energy grows, the demand for efficient, high-performance energy storage systems becomes increasingly critical, especially in uninterruptible power supplies (UPS), where reliability and fast transitions are essential. Supercapacitors, with their high power density and rapid charging capabilities, are emerging as strong alternatives to lithium-ion batteries in UPS systems. This paper presents a novel real-time estimation technique for monitoring supercapacitor parameters within a UPS, focusing on the dynamic behavior of these parameters and their evolution over the system’s lifecycle. The proposed estimator demonstrates exceptional accuracy, achieving less than 1% error within 120 ms of startup and nearly zero error thereafter. The estimator’s performance remains robust even as supercapacitor parameters change due to aging effects over the lifespan. The UPS system features a modular design, enabling scalability to accommodate higher power requirements or longer backup durations and adaptability to various supercapacitor types. Experimental results highlight the system’s robustness in both charging and backup modes, emphasizing the potential of supercapacitors as key components in future UPS systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".