Hybrid Cars Setting New Challenges for Optimized Power Semiconductors
Bibliographic record
Abstract
The electrification of the powertrain is still one of the main challenges and innovation drivers for modern cars. With the introduction of the Toyota Prius, launched in Japan in 1997 the first commercially available hybrid car in mass production, the development continued towards the BMW i3 launched in July 2013. One main component for all kind of hybrid cars is still the power semiconductor, which is used for DC/DC converters and for the inverter to drive the electric motor for the traction control. What makes the selection of the right power semiconductor complex, is the variety of different voltage levels within the car (from standard 12V board net, the new 48V board net all the way up to 400V and above) plus different requirements in terms of switching and conduction performance, or accordingly power losses. The selection of device by application and voltage will be discussed in this paper. By comparing and contrasting the technology differences of MOSFETs and IGBTs, a recommendation for the ideal power device for different applications will be given. Typical trade off diagrams including switching frequency and breakdown voltage will help the designer to select the right device at a glance.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".