Application of Regenerative Braking with Optimized Speed Profiles for Sustainable Train Operation
Bibliographic record
Abstract
This paper presents a method for synergizing the energy-saving strategies of integrated coasting and regenerative braking in urban rail transit operations. Coasting saves energy by maintaining motion with propulsion disabled, but it induces longer travel time. Regenerative braking captures and reuses the braking energy of the train and could shorten travel time but reduces the time available for coasting, indicating a tradeoff between the two strategies. A simulation model was developed based on fundamental kinematic equations for assessing sustainable train operation with Wayside Energy-Saving Systems (WESSs). The objective of this study is to optimize speed profiles that minimize energy consumption, considering the train schedule and specifications, track alignment, speed limit, and the WESS parameters such as storage limit and energy losses in the transmission lines. The decision variables are the acceleration at each time step of the respective motion regimes. Since the study optimization problem is combinatorial, a Genetic Algorithm was developed to search for the solution. A case study was conducted which examined various scenarios with and without WESS on a segment of an urban rail transit line to test the applicability of the proposed model and to provide a platform for the application of ideas developed in this study. It was determined that synergizing the energy-saving strategies of coasting and regenerative braking yielded the greatest efficiency of the scenarios examined.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".