Integrated Multipurpose Power Electronics Interface for Electric Vehicles
Bibliographic record
Abstract
A novel integrated multipurpose power electronics interface (IMPEI) for the new generation of plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs) is proposed in this article. The IMPEI is a reconfigurable power electronics interface (PEI) that integrates the onboard charger with the drive inverter allowing the same interface to be used for various modes of operation such as propulsion and regenerative braking as well as vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations with grid flexibility. For each mode of operation, the IMPEI is reconfigured into a previously existing power converter topology. The principles of operation and coordination of various modes of operation of the IMPEI are explained in this article. Based on component count, operating modes, and control complexity, a comparison of the IMPEI and recently proposed integrated PEIs (IPEIs) is provided. Specifications of BMW i3 are used as the benchmark for comparing size, cost, and efficiency. This article discusses and validates the experimental results.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".