Thermal Design Methodology of Power Converters for Electric Vehicle Applications
Bibliographic record
Abstract
With increasing awareness of climate change, governments and organizations have made it their mission to see a greener future. Countries like Norway, South Korea, and Canada have promised to ban internal combustion engines (ICE) by 2025-2035. Growing demand for cleaner modes of travel have taken over the market, causing everyone to look at electric vehicles for the solution. Tesla’s revenue has tripled in the past five years, 15 new electric car manufacturer shave joined, and almost all big-name ICE companies have started producing electric/hybrid cars. As the number of electric vehicles increases, a solution to long charging times will be needed to keep up with the high-power-density fuel used in ICE. Charging stations are increasing in power ratings as Tesla introduces their 250-kW supercharger and EVBox with their 350-kW Ultronig stations. These stations are comprised of power modules that stack together to reach the desired power rating. Designing, testing, and implementing power modules for electric vehicles can be a complex process due to thermal efficiency and packaging challenges. To address these issues, it is essential to establish a design methodology for power modules that takes into account validation and packaging considerations. This thesis presents a design methodology for heat exchangers that allows for rapid prototyping with sufficient accuracy, approximately below 10%. The study includes numerical simulations, reduced modeling, and experimental validation, which can increase confidence during the design phase and reduce design times. Using reduced models for quick calculations instead of relying solely on numerical models can further expedite the process. A reliable and adaptable analytical methodology for heat exchanger design is crucial for successful optimization setup.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".