A Survey on Electric Buses—Energy Storage, Power Management, and Charging Scheduling
Bibliographic record
Abstract
In recent years, aiming to reduce the metropolitan air pollution caused by fossil fuel-powered vehicles, the electrification of transportation, such as electric vehicles (EVs) and electric buses (EBs), has attracted great attention from the automobile industry, academia, and public transportation. EBs, driven by decarbonized electricity, can reduce the air pollution and noise level. Besides, they can also recover electricity from regenerative braking. Recent years have witnessed continuous works on the topics of EB energy storage, power management, and charging scheduling. In this review, we have comprehensively surveyed three primary parts: important components; existing research topics; and open issues of EBs. Specifically, we first introduce the important components of EBs, including energy storage systems, powertrains, interleaving elements and electric motors, and driving cycles. Then, we review the existing research topics of EBs, including the energy storage system sizing, power/energy management, range remedy methods, charging design/scheduling, and trial projects. At last, extending from existing literatures, we further propose the future research opportunities and ongoing challenges, such as extending EV related research to EBs, EB charging demand modeling, and EB impact on power systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".