Loss Modeling and Testing of 800-V DC Bus IGBT and SiC Traction Inverter Modules
Bibliographic record
Abstract
This paper investigates efficiency gains achieved using an 800 V DC bus and wideband gap silicon carbide (SiC) semiconductors for a light-duty electric vehicle (EV), rather than an insulated-gate bipolar transistor (IGBT) inverter with a 400 V bus as is commonly used for EVs. Analytical inverter loss models with 600 V and 1200 V IGBTs, and 1200 V hybrid SiC and 1200 V All-SiC semiconductors are incorporated into a Chevrolet Bolt EV model and simulated over standard drive cycles. Battery pack voltage variations throughout the drive cycles, as well as variations in junction temperature, resulted in 16 to 27 % increased loss compared to fixed voltage and temperature assumptions. To validate the models, experimental testing was performed on a 1200 V IGBT inverter and a 1200 V SiC inverter both powering 160+ kW rated traction machines. Experimentally measured loss was typically within 100 W of the model, demonstrating its accuracy. Going from a 400 V to an 800 V DC bus with IGBTs, EV range was modeled to increase 1.2 %, while an 800 V bus and all SiC inverter results in a range increase of 5.0%. An empirical loss model fitted to measured inverter data shows the analytical model estimates range within 6 km.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".