Overview and Comparative Assessment of Single-Phase Power Converter Topologies of Inductive Wireless Charging Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The acquisition of inductive power transfer (IPT) technology in commercial electric vehicles (EVs) alleviates the inherent burdens of high cost, limited driving range, and long charging time. In EV wireless charging systems using IPT, power electronic converters play a vital role to reduce the size and cost, as well as to maximize the efficiency of the overall system. Over the past years, significant research studies have been conducted by researchers to improve the performance of power conversion systems including the power converter topologies and control schemes. This paper aims to provide an overview of the existing state-of-the-art of power converter topologies for IPT systems in EV charging applications. In this paper, the widely adopted power conversion topologies for IPT systems are selected and their performance is compared in terms of input power factor, input current distortion, current stress, voltage stress, power losses on the converter, and cost. The single-stage matrix converter based IPT systems advantageously adopt the sinusoidal ripple current (SRC) charging technique to remove the intermediate DC-link capacitors, which improves system efficiency, power density and reduces cost. Finally, technical considerations and future opportunities of power converters in EV wireless charging applications are discussed.
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.
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) | 0.001 | 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) | 0.000 | 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 it