Start‐up circuit for an electric vehicle fast charger using SSICL technique and a slow estimator
Bibliographic record
Abstract
With the increase in power rating of electric vehicle chargers, concerns regarding their integration to the grid have likewise grown. This paper investigates a novel technique for start‐up of high‐power fast electric vehicle (EV) charger using a solid‐state inrush current limiter (SSICL). The circuit consists of three similar sets of bidirectional switch and limiting resistor that utilizes a control strategy based on a simple Kalman filter (KF) to suppress the converter start‐up current and transient over‐voltages. In fact, the built‐up profile of the inrush current is dictated in accordance with the estimated current by the slow dynamic KF. Compared to alternatives, such as start‐up resistors, the presented SSICL achieves faster start‐up with comparable peak current, tends to provide lower distortion, and provides a good balance between speed and efficiency. Simulation and experimental results confirm that the proposed method can considerably suppress inrush currents, while improving the total harmonic distortion. Comparing the behaviour of the SSICL to other relevant techniques underlines the advantages of the proposed solution. Furthermore, although the focus of this paper is start‐up of the fast EV chargers, the proposed circuit can act as an inrush‐current limiter in most AC to DC converter structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".