Investigation of a Kelvin-Source Connected SMD Silicon MOSFET and its Application in a High Current Full Bridge Synchronous Rectifier
Bibliographic record
Abstract
Auxiliary loads in an electric vehicle (EV) are in the order of 12–48 V since voltages above 50 V pose risks to the passengers. Power demand on auxiliary loads continues to expand due to increased computation requirements and load profiles. This requires development of power conversion systems that can manage high-currents efficiently. Construction of high current power conversion systems require multiple power switches (MOSFETs) in parallel, which reduce conduction losses. Efficiency can be further improved by incorporating Kelvin-source connections due to improved decoupling of the gate-source (G-S) and drain-source (D-S) loops in a MOSFET. This paper proposes a method to create a Kelvin-source connection in SMD silicon (Si) MOSFET packages with multiple source terminals. A comparison of switching energies between the conventional and proposed methods to highlight the improvements is performed. A 10–14 V/2.8 kW capable full bridge (FB) synchronous rectifier is constructed for application in a DC-DC converter. Parallel connected HDSOP-16 Si MOSFET packages are used to construct this rectifier with the proposed Kelvin-source and imbalances of the root-mean-square (RMS) current ($I_{RMS}$), turn-on ($I_{on}$) and turn-off ($I_{off}$) currents, resultant power loss profile and effect on the design's thermal gradient are studied.
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".