An Accurate Loss Model of Single-Stage Single-Phase Isolated PFC Converter for Bidirectional Plug-in EV Charger
Bibliographic record
Abstract
In recent years, the power handling capability of EV chargers has been growing to reduce the charging time. In order to enhance the design and efficiency of EV chargers, an in-depth investigation of the losses caused in plug-in EV chargers is needed. This article presents an accurate loss model of a novel single-stage single-phase isolated PFC converter for a bidirectional EV charging application. The EV charger includes a current-fed full-bridge converter with bidirectional switches on the grid side with a swinging boost inductor that is affiliated with a full-bridge converter at the dc-side coupled via a high-frequency transformer. The AC-side current can be controlled to obtain power factor correction with low current total harmonic distortion (THD). The AC-side switches are naturally commutated and attained zero current switching (ZCS) without any external passive components. Additionally, zero current turn-ON is accomplished for DC-side switches to realize a high-efficiency EV charger. This is achieved with a modified control strategy and novel modulation method are adopted for achieving soft-switching operation and bidirectional power flow. To ensure that the proposed EV charger is feasible, experimental results with loss analysis of a 1.5 kVA unit are presented showing that the operation, analysis, and design are perfectly accorded.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".