From Off-line to On-line Control of a Multimode Power Split Hybrid Electric Vehicle Powertrain
Bibliographic record
Abstract
On-line optimal control represents a crucial issue in the development of multimode power split hybrid electric vehicles (HEVs). Finding a control strategy that guarantees fuel economy optimality and ease of implementation still reveals an open research question. This paper aims at developing an on-line control approach for multimode HEVs based on previously implemented off-line control. The two control levels for multimode HEVs are presented: the operating mode selection and the torque split determination. The former is addressed adopting a machine learning approach where artificial neural networks (NNs) are trained in supervised learning using off-line control data. The torque split is resolved on-line according to efficiency-based maps extracted off-line. Simulation results for a specific multimode HEV design demonstrate the effectiveness of the developed control strategy in minimizing the value of predicted fuel consumption. Furthermore, a sensitivity study is conducted for the NN sizing parameters. The ease of implementation and adaptability suggests the potential application of the developed online control approach in a design methodology for multimode HEVs.
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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".