A Low Impact V2H Battery Charging Station Using an AC Green Plug Switched Filter Scheme
Bibliographic record
Abstract
This work proposes a switched/modulated capacitor filter approach for optimizing electric vehicle (EV) and vehicle-to-house (V2H) battery-charging stations. The proposed method employs using classical optimized Type- 2 Fuzzy Logic Controller. Multi-mode charging modes are modified to enable super-fast charging and enhanced power quality while reducing voltage transients on the DC side, inrush currents and harmonics on the AC side. An inter-coupled AC-DC capacitor interface equipped with dual complementary switching modes is utilized by the modulated filter to enable capacitive compensator pulsing and dual operational modes for the tuned arm filter. The aim of the proposed switched inter-coupled AC-DC filter compensation strategy is to achieve EV-enhanced electrical power usage by mitigating AC-DC voltage transients and inrush currents. The paper presented a dual action switched capacitors-tuned filter and reactive compensator scheme. The dual action complementary switched filter/capacitor compensator is controlled by a multi loop regulation controller to ensure effective measured action based on the global errors sum of the voltage and current/power loops. The novelty of the paper lies in using a multi loop weighted error action to carry the Pulse-Width Modulation (PWM) power width modulation - duty cycle ratio based on slow changing dynamic/inrush loads. Results show the importance of the proposed scheme for improving the overall system efficiency and quality. The proposed scheme is tested and validated under different operating conditions and proved its effectiveness for various conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".