Flexible Power Control for Dual-Source Inverters in Electric Vehicles
Bibliographic record
Abstract
Different topologies of multi-source inverters (MSIs) have been widely studied in the literature These inverters integrate multiple DC sources to supply an AC load. In electric vehicles (EVs), batteries and ultracapacitors (UCs) are the preferred choices, with complementary characteristics, and they enable the recovery of regenerative energy from the output. Due to single-stage conversion, efficiency is significantly enhanced. For the dual-source version of MSIs, a simple SVM-based modulation technique has been proposed. This approach utilizes similar vectors but different DC-link voltages to generate the reference vector. Although straightforward, this modulation technique does not perform optimally in all real-world scenarios, leaving room for improvement. This paper introduces an extended operating modes of the conventional modulation that takes into account practical considerations. The computational burden of the extended modulation remains as low as that of the conventional approach while offering additional features, such as optional battery charging and discharging and more flexible power allocation. Both extended and conventional modulation techniques are explained in detail, and simulation and experimental results are provided to validate the effectiveness of extended modulation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".