Energy Recovering Using Regenerative Braking in Diesel–Electric Passenger Trains: Economical and Technical Analysis of Fuel Savings and GHG Emission Reductions
Why this work is in the frame
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Bibliographic record
Abstract
Rail transport, specifically diesel–electric trains, faces fundamental challenges in reducing fuel consumption to improve financial performance and reduce GHG emissions. One solution to improve energy efficiency is the electric brake regenerative technique. This technique was first applied on electric trains several years ago, but it is still considered to improve diesel–electric trains efficiency. Numerous parameters influence the detailed estimation of brake regenerative technique performance, which makes this process particularly difficult. This paper proposes a simplified energetic approach for a diesel–electric train with different storage systems to assess these performances. The feasibility and profitability of using a brake regenerative system depend on the quantity of energy that can be recuperated and stored during the train’s full and partial stop. Based on a simplified energetic calculation and cost estimation, we present a comprehensive and realistic calculation to evaluate ROI, net annual revenues, and GHG emission reduction. The feasibility of the solution is studied for different train journeys, and the most significant parameters affecting the impact of using this technique are identified. In addition, we study the influence of electric storage devices and low temperatures. The proposed method is validated using experimental results available in the literature showing that this technique resulted in annual energy savings of 3400 MWh for 34 trains, worth USD 425,000 in fuel savings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it