Improving Fault Current Interruption Capability of Contactors in EV Battery Packs with SiC MOSFET Commutation Circuit
Bibliographic record
Abstract
A Hybrid Circuit Breaker (HCB) solution is proposed to improve the DC fault capability of the DC contactors for Li-ion battery protection in EVs. The common short-circuit protection of EV battery packs is achieved through fuses, while the DC contactors are not supposed to switch any current. However, the intrinsic levitation force of DC contactors can separate the contacts during short circuit events where ares are generated and damage the contactors. To protect contactors damage from this behavior, the pack fuse selection has to be either fast or controllable, so that the fault current can be interrupted before levitation happening. This lays the motivation of this paper. The proposed approach uses a 1200V/700A SiC MOSFET power module to commutate the fault current and help extinguish the contactor arc due to its levitation under the high fault current. A prototype is designed, built, and tested to 400V/600A. Compared to the prior art contactor, the HCB can potentially eliminate the need of a series fuse and extend the lifetime even for normal load current switching.
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 | 0.001 |
| Open science | 0.001 | 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".