Performance Evaluation of Single-Phase On-Board Charger with Advanced Controller
Bibliographic record
Abstract
Abstract: The increasing mobility of electric vehicles has inspired vehicle growth to power grid technology. Such as vehicle to grid technology allows to transfer the power from the electric vehicle battery to the power grid. This enable speak load shaving, load leveling, voltage regulation, and improved stability of the power system. To develop the vehicle to grid technology requires a specialized EV battery charger, which permits the bi-directional energy transfer between the power grid and the electric vehicle battery. There is a specific control strategy used for a bi-directional battery charger. The proposed control strategy is used for charge and discharge battery of EV. The charger strategy has two parts: 1) Bidirectional AC-DC Converter in two-way Communication System. 2) Bidirectional DC-DC Buck-Boost Converter. There are two modes of operation for a bidirectional ac-dc converter: for G2V, rectifying mode is used, and for V2G, inverter mode is used. The suggested charge strategy not only allows for two-directional power flow but also provides power quality management of the power grid. Fuzzy logic controller (FLC) transforms linguistic control topology evaluations knowledge into an automated control topology using FLC. The FLC is more stable, has less overshoot, and responds quickly. The operation of a standard PI controller and a FLC was compared in this study using MATLAB and Simulink, and different time domain characteristics were compared as toshow that the FLC had a smaller overshoot and a faster response than the PI controller. Keywords: Bi-directional AC-DC converter, bi-directional DC-DC Buck-Boost converter, electric vehicles (EVs), on-board battery charger (OBC), grid to vehicle (G2V), vehicle to grid (V2G).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".