Power Sharing Control Algorithm for Direct Integration of Fuel Cells in a Dual-Inverter Electric Vehicle Drivetrain
Bibliographic record
Abstract
Fuel cell (FC)-battery hybrid electric vehicles (EVs) are an alternative to emergent battery EVs. Normally, a dc–dc converter is used to connect the low-voltage dc output of the FC to the high-voltage EV battery and ensure that a slow-changing unidirectional power flow is maintained from the FC. This article proposes to directly integrate the FC as one of the two energy sources in a dual-inverter-based EV drive. A conventional EV battery is used as the second energy source. A power sharing algorithm is introduced, which allows the dual-inverter drive to be modulated such that unidirectional power flow is maintained from the FC. In addition, power flow is controlled such that the minimum FC power to avoid its unnecessary shutdown is maintained under all operating points of an EV drive cycle, including regenerative braking. A key aspect of this algorithm is the injection of motor reactive current as a means to achieve desired FC power transfer during fast mechanical power reduction transients or when low mechanical power is required from the drive. This method allows for the motor to achieve fast mechanical power transients while respecting the slowly changing power reference of the FC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".