Battery Charging by Using a Multi-Tapped Secondary Coil
Bibliographic record
Abstract
This paper presents an inductive coupling system used to recharge the batteries of electric vehicles (EV) as well as the ultra-capacitors used as energy storage elements wirelessly. Although ultra-capacitors can quickly charge, the standard inductive coupled circuits provide maximum power only for specific load impedance. The value of this impedance depends on the geometrical dimensions of the coils, coils configuration, as well as the distance between them. Since an ultra-capacitor in the charging mode is equivalent to an instantaneously increasing impedance, the maximum power supplied to the ultra-capacitor involves a single point inside the charging range, resulting in better optimum charging time. The analysis of the inductive coupling theory reveals that the optimal load impedance can vary by adjusting the secondary coil inductance and the resonant tuning capacitance. The proposal to modify the optimal load impedance dynamically throughout the capacitor charging range consists of a three-tapped secondary coil. Furthermore, this paper aims to design and analyze optimal wireless battery loading systems for electronic devices. The construction of these optimal systems uses an inductive coupling architecture that has a multi-tapped secondary coil (receiver). In this case, there are multiple magnetic couplings between the emitting coil and the receiving coils and even between the receiving coils corresponding to each tap. Consequently, results in a dynamic change of the coils geometries to modify the optimum load impedance and maximize the active power transmitted to the load. A thorough comparison involving performances put face to face the wireless power transfer systems (WPTS) containing only two magnetically coupled coils with those with multi-tapped secondary coils (MTSC). The main conclusion reveals the advantages of inductive architecture with MTSC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".