Analysis and Design of Soft-Switching Active-Clamping Half-Bridge Boost Inverter for Inductive Wireless Charging Applications
Bibliographic record
Abstract
This paper presents an accurate analysis and a design methodology for a fixed-frequency-controlled active-clamping half-bridge boost inverter (HBBI)-based series-series compensated inductive power transfer (SS-IPT) charging system. First, the operation principles of the active-clamping HBBI in the charging system are analyzed. Consequently, both the steady-state model and the small-signal model are correctly derived by using the extended describing function (EDF) method and are used to design the system. The derived steady-state model is employed to develop a new design approach to achieve zero-voltage switching (ZVS) for the inverter. The dynamic behavior of the system is investigated, and a digital controller for charging current regulation is designed based on the derived small-signal model. The proposed methodology enables not only reducing switching losses but also avoiding bifurcation. Finally, a 1-kW laboratory prototype is implemented to verify the accuracy of the theoretical analyses. Simulation and experimental results demonstrate that the control method can effectively regulate the charging current with a fast response and no steady-state errors and can enable the inverter to achieve ZVS over a wide variation of the charging current and the battery voltage. The hardware prototype achieves a peak dc-to-dc efficiency of 93.4% at a 170-mm air gap, which is comparable to other IPT systems at similar power levels in the literature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".