Broadly-Applicable Accurate Analytical Steady-State Model for Class-E Inverters
Bibliographic record
Abstract
This paper presents an accurate closed-form analytical steady-state model for class-E inverters that is broadly applicable. This model is developed using a recently proposed modeling approach for power converters termed Laplace Based Steady-State Modeling (LBSM). Following the LBSM approach, the dynamics of the class-E inverter are modeled using an ordinary differential equation (ODE) with periodic and discontinuous inputs expressed in terms of one of its state variables, and the initial values of the state variable and its derivatives under steady-state operation are determined in closed-form, enabling the determination of closed-form expressions for the steady-state waveforms of the class-E inverter. The presented model is validated using simulation and experimental results for a conventional large ("choke") input inductor class-E inverter. The value of the proposed model is demonstrated by using it to design a finite input inductor class-E inverter in which the inductance of the input inductor is selected such that it helps achieve zero-voltage switching (ZVS) for a range of loading conditions. It is shown that the proposed approach results in a design with a more appropriate inductance value for the input inductor than previous approximate design approaches based on fundamental harmonic analysis or simulations.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".