V2X Operation of Integrated Single-Phase Bidirectional Electric Vehicle Charger
Bibliographic record
Abstract
The higher cost of electric vehicles (EVs) compared to combustion vehicles is a roadblock to the adoption of EVs. As the higher cost is mainly due to the battery pack, it cannot be significantly reduced, however it can be made a better value if the EV can provide additional functionalities. Vehicle-to-everything (V2X) operation can allow the EV to operate as a mobile power source, eliminating the need for separate backup/portable generators and standalone power inverters. This paper presents the V2X operation of an integrated single phase charger based on the dual-inverter drive. As this integrated charger re-uses the existing high-power traction inverter and motor, this saves additional costs over previously presented V2X capable discrete on-board chargers, while offering a higher power output. This paper describes the principle of operation and control, along with experimental results using a full-scale 110 kWpkdual-inverter drive consisting of a liquid cooled EV motor. Real world functionality is demonstrated by powering resistive loads and non-linear loads, as well as a jigsaw power tool. Finally, efficiency measurments are provided, with a peak efficiency of 96%.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".