Operation of Inductive Charging Systems Under Misalignment Conditions: A Review for Electric Vehicles
Bibliographic record
Abstract
Inductive power transfer (IPT) for electric vehicles (EVs) is an emerging technology that can transfer power wirelessly over certain distances, thus offering some remarkable characteristics in terms of flexibility, position, and movability. The output power of an IPT system depends on the coupling factor of the magnetic couplers, which can deviate from the nominal operating conditions due to the occurrence of misalignment. Nevertheless, misalignment of the magnetic couplers in inductive charging is inevitable, and it usually results in the variation of the mutual inductance and output power of the system with a corresponding decrease in the system’s overall efficiency. So far, the literature has reported various techniques for achieving designs with higher misalignment tolerance. The reported techniques can be mainly classified into three categories, as viewed from the following aspects: magnetic couplers layouts, compensation networks, and control strategy. Each of these techniques has its pros and cons in terms of implementation cost, system layout, efficiency, power density, and reliability depending on the application. With the increased investigation of more applications of IPT, new modified techniques for improving the misalignment tolerance in the IPT system are continuously being proposed based on permutations and combinations of the existing ones; thus causing some confusion and difficulties for researchers and system vendors to follow. This article, therefore, aims to provide a comprehensive review of the existing methods for IPT systems that address the misalignment issue in EVs’ wireless charging. The background of the inductive charging systems for EVs is presented and an investigation of the numerous factors affecting the output power and other performances is conducted. In addition, the advantages and disadvantages of each technique on the IPT system’s performance are analyzed in detail.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".