Modeling and Simulation of Voltage-Controlled DC-DC Converters Using Dynamic Phasors
Bibliographic record
Abstract
Modeling and simulation of DC-DC converters are essential in the design of efficient and robust energy systems, telecommunication equipment, transportation infrastructure, and healthcare devices. This paper presents computationally efficient frequency-dependent average models (FDAMs) of voltage-controlled DC-DC converters (buck, boost, and buck-boost) developed using the dynamic phasor (DP) method. By recognizing that in a DC-DC converter, the DC component dominates in the circuit variables, this paper neglects the pulse-width modulation (PWM) stage in developing control algorithms used in generating time-varying duty cycle unlike in the existing FDAMs. Rather, the time-varying duty cycle is produced by using the output voltage zeroth DP component as feedback signal while neglecting the high frequency components. This approximation simplifies the control process thereby enabling faster simulations of the FDAM models. Comparative studies involving step changes in output voltage reference and the load resistance are conducted using the detailed DC-DC converter models implemented in Simulink/Simscape (SS) platform, and the DP-based FDAMs developed in MATLAB environment in order to validate the proposed DP model's control scheme. Simulation results obtained reveal that the DP-based models are capable of accurately depicting the switching transients and steady-state conditions as the fully detailed switched models developed in SS while being more computationally efficient than the SS models. Thus, the DP-based FDAM is suitable for conducting a detailed system-level study of DC-DC converter-based power systems.
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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 | 0.000 | 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".