A New Regen-based Energy Management Strategy for Online Control of Hybrid Powertrains
Bibliographic record
Abstract
The increasing necessity of manufacturing electrified vehicles also represent an increase in developing controllers that govern such powertrains. This paper uses the equivalent consumption minimization strategy (ECMS) method as the backbone of a novel approach named regen-based equivalent consumption minimization strategy (R-ECMS). The optimization-based algorithm accounts for the charge provided by the regenerative braking when computing the equivalent fuel cost. Although based on the ECMS, the R-ECMS does not share many of the usual limitations, such as having its performance constrained to drive cycles it is optimized for, and the necessity of having extra control rules for charge depletion situations. The results show that the R-ECMS method is more flexible and able to deliver charge sustaining and fuel efficient performances, which is key for plug-in hybrids. Besides, its fuel efficiency for the optimized case has shown to be slightly higher than the fuel efficiency achieved by the ECMS algorithm for the same optimized mission. Nonetheless, research on brake profile prediction must be conducted to allow leveraging the benefits of the R-ECMS algorithm in real applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".