Comprehensive review of PV/EV/grid integration power electronic converter topologies for DC charging applications
Bibliographic record
Abstract
In recent years, there has been great interest in the research and development of electric vehicles (EVs). In the past 5 years, numerous EVs and plug-in hybrid electric vehicles (PHEVs) have been introduced in the market by auto manufacturers. However, with the increase in demand of EVs/PHEVs comes the need to provide charging facilities at convenient locations, for recharging of the on-board batteries. Fast charging is also a necessity for consumer convenience. Therefore, off-board charging infrastructures have to be looked at closely. This need for fast charging is creating a shift in EV charger development work to focus on DC charging facilities. This paper provides an overview of various power converter topologies used for a photovoltaic (PV) and grid connected DC charging infrastructures. Specific focus is placed on Z-source converters that can be used for a single-stage connection of PV, grid, and EV. PWM control techniques for off-board converters are also reviewed in this paper.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.009 | 0.006 |
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".