Review of inductive power transfer technology for electric and plug-in hybrid electric vehicles
Bibliographic record
Abstract
For the last two decades, significant improvements in charging technologies have been made. Moreover, novel applications have been proposed and tested, obtaining important and promising results. Inductive charging for electric vehicles (EV) and hybrid electric vehicles (HEV) is one of them. Because of the positive impact that this technology represents, it is important to understand the general characteristics of this novel application. This paper aims to give a general understanding of inductive charging systems for EV and plug-in HEV. The explanation of what is an inductive power transfer (IPT) transformer and how electrical power is transferred through air is also presented. The review of the electrical characteristics of an IPT transformer is shown: derivation of equations and presentation of the equivalent circuit. The analysis of the series-series (SS) compensation topology is covered. Additionally, to validate the theoretical concepts, an IPT transformer setup with a 5cm air gap is simulated. The simulation results where as expected from the theory. The power transfer capability of the system was increased from 0.05 W, with no capacitive compensation, to 87.6 W, with capacitive compensation. The plots and circuit simulations where obtained in MATLAB and SIMULINK respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".