SoH-Aware Charging of Supercapacitors With Energy Efficiency Maximization
Bibliographic record
Abstract
Recent years have seen significant advances in supercapacitor-based applications in portable electronics, where the switching resistor circuit acts as a common cell balancing circuit when charging the supercapacitors. However, existing charging control methods suffer from low energy efficiency, leading to considerable energy loss, and thermal heating. In this paper, we propose a state-of-health (SoH)-aware energy-efficient charging method to maximize the energy efficiency of supercapacitors during the charging process. First, we provide a sufficient and necessary condition to maximize the energy efficiency. Then, an online SoH estimation algorithm is designed to estimate capacitance and balancing resistance in real time. Thereafter, an SoH-aware energy-efficient charging algorithm is further proposed to be implemented in microcontrollers. A charger prototype has been built to verify the effectiveness of the proposed charging algorithm. Extensive simulation and experiment results show that the energy efficiency of the proposed design is improved considerably when compared with existing 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 | 0.000 | 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 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".