An MHO Approach for Electric Bus Charging Scheme Optimization Based on Energy Consumption Estimation
Bibliographic record
Abstract
In recent years, the increasing concerns on environmental issues have promoted the development of alternatives for urban mobility and public transportation. Specifically, the electric buses (EBs) have attracted significant attention due to the flexibility, sustainability, and excellent performance in terms of reducing greenhouse gas emissions. Because of the capital cost of the EB batteries, how to optimize the charging scheme of EBs in order to extend the battery lifetime is a major challenge faced by the public transit service providers. This challenge is complicated due to the highly dynamic and random energy consumption in the driving process, which is caused by engine torque, friction force, aerodynamic drag force, road condition, real-time speed, acceleration, and other related physical parameters. In order to address this challenge, a moving horizon optimization (MHO) approach is developed in this paper to optimize the charging scheme and extend the battery lifetime. Based on a detailed physical model involving the aforementioned stochastic variables, an energy consumption estimation model is proposed as a significant step in the optimization process. The performance of the proposed approach is evaluated via extensive simulations based on the actual Google transit data from Edmonton Transit Service (ETS).
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".