Analysis of LLC Resonant DC-DC Converter with Non-Singular Fast Terminal Sliding Mode Control for Ev. Battery Charging
Bibliographic record
Abstract
This paper introduces a non-singular fast terminal sliding mode (NFTSM) control approach for a novel full-bridge LLC unidirectional resonant a step-up DC-DC converter to improve the dynamic performance and stabilize the output voltage of the system.It is robust and suitable for charging high-power lithium-ion (Li-ion) batteries in Electric Vehicles (EVs).This converter is a new modification for a full-bridge LLC resonant DC-DC converter by connecting two converters in parallel with six MOSFET power.The power switches of the converter operate under soft-switching and Zero Current Switching (ZCS).Extended Description Function (EDF) is utilized to simplify large-signal model.A detailed analysis and theoretical approach are presented using linearization of second-order small-signal model.A closed-loop NFTSM control system is designed to provide regulated output voltage under a wide variation of input voltage.The measured results reveal that a high dynamic performance feature is obtained under multiple disturbance factors.The system operates at 100 kHz resonant frequency with various input voltage to achieve constant output voltage of 400 V.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".