Electro-Thermal Codesign Methodology of an On-Board Electric Vehicle Charger
Bibliographic record
Abstract
Abstract Significant advances are needed to optimize the charging speed, reliability, safety, and cost of today's conservatively designed electric vehicle (EV) charging systems. The design and optimization of these novel engineering systems require concurrent consideration of thermal and electrical phenomena, as well as component and system level dynamics and control to guarantee reliable continuous operation, scalability, and minimum footprint. This work addresses the concurrent thermal and electrical design constraints in a high-density, on-board, bidirectional charger with vehicle-to-grid (V2G), grid-to-vehicle (G2V), vehicle-to-house (V2H), and vehicle-to-vehicle (V2V) power transfer capabilities. The electrical design of this charger consists of DC–DC and DC–AC power stages connected in series. The power-stage circuits are implemented on a printed circuit board (PCB) with 16 surface-mount silicon carbide MOSFETs, three inductors, and one transformer. The main goal of this work is to investigate the interplay between the cooling architecture and the PCB layout, and the corresponding impact on the heat dissipation and parasitic inductance. This work compares the performance of three generations of this multifunctional charger that employ different design methodologies and proposes high-level design guidelines derived from multiphysics simulations and experimental tests.
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) | 0.003 | 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".