Energy management strategy to optimise regenerative braking in a hybrid dual‐mode locomotive
Bibliographic record
Abstract
This study proposes an energy management strategy (EMS) for a dual‐mode hybrid locomotive equipped with a fuel cell, supercapacitors, and batteries, and intermittent access to an electrified overhead catenary. It is inspired by the Ragone plot and does not consider information or predictions of future load consumption. It aims to reduce a cost function that considers the cost of hydrogen, the electricity consumed from the network, and the energy sources' degradation. The EMS focuses on maximising the energy recovered during braking. The study introduces a methodology to tune the EMS parameters. Two study cases are used to evaluate the EMS. In the evaluation driving profile, typical for a French freight train, the braking energy is around 12.8% of the total energy. With the proposed EMS, the energy recovered is around 99.8% of the total braking energy. A second EMS not oriented to reduce the energy in the braking resistor is also evaluated. The energy recovered with this strategy is around 91.5% of the total braking energy. The global energy reduction is around 1.1% compared with the second EMS and 12.8% without energy recovering. These results show a real opportunity to increase the energy recovered during braking.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".