On the concept of a novel Reconfigurable Multi-Source Inverter
Bibliographic record
Abstract
Nowadays, the two competing powertrains used in Hybrid and Plug-in Hybrid Electric Vehicles are embodied by the Chevrolet Volt and the Toyota Prius. These powertrain architectures are costly which is primarily due to the fact that they either use a large and expensive battery pack or a smaller battery pack coupled to a power converter. The concept of a novel Reconfigurable Multi-Source Inverter (ReMS) is introduced in this paper where two or more DC sources with variable voltages are interconnected to a three phase output load. It is capable of connecting the two DC sources in a parallel or series configuration allowing the powertrain to reduce switching losses as well as sustain peak torque for higher motor speeds, which, in turn, allows for a reduction in battery size. A modified Space Vector Pulsewidth Modulation (SVPWM) scheme is used to control the ReMS where three phase AC waveforms are synthesized from 24 non-zero voltage vectors. Simulation results of the ReMS applied in a hybrid electric powertrain are conducted where a rectifier and Li-ion battery act as the two DC sources. Modes of operation, line-to-line voltages, the sinusoidal three phase currents and the state of charge of the battery are plotted.
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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 | 0.000 |
| 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 | 0.001 |
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".