A DLL/PLL based multi-phase interleaved DC-DC converter with digital off-time control and active series balancing for electric vehicles
Bibliographic record
Abstract
In this paper, a Delay-Locked Loop (DLL) and Phase Locked Loop (PLL) interleaving control scheme is demonstrated for multi-phase dc-dc converters in a light electric vehicle (LEV) application. Three interleaved non-inverting buck-boost (NIBB) sub-converters operate in peak current mode control (PCMC), and a fully digital off-time generator (OTG) is used in lieu of slope compensation for more accurate battery current sensing and improved converter dynamics. A DLL adjusts the master sub-converter off-time to maintain quasi fixed-frequency operation, while a PLL adjusts the two slave sub-converter off-times to achieve inductor current interleaving. Active series balancing is achieved by adding a single switch to the NIBB that connects to the adjacent series battery. The modified topology reuses the NIBB inductor to reduce system cost, complexity and weight, while providing balancing capabilities. The series balancing scheme is validated in simulation, and the DLL/PLL interleaving control schemes are demonstrated experimentally on a 12 V, 270 W dc-dc converter running at 555 kHz. With the DLL and PLL based frequency control, multi-phase dc-dc converter interleaving is achieved within 150 μs, and stable QFF operation and interleaving are maintained through step-up and step-down transient events.
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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) | 0.000 | 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 | 0.001 |
| 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".