Electric Bus State-Of-Health Aware Cost Analysis Given Energy Consumption and Initial Battery Purchase Price
Bibliographic record
Abstract
This paper presents an analysis of the cost implications of varying the battery pack size in electric buses designed for public transportation, with a focus on buses. The study compares buses equipped with three distinct battery packs, each differing in capacity and weight while sharing the same cell parameters. The state-of-health (SOH) of the battery is determined by assessing the number of cycles it can complete before its capacity declines to 80% of that of a new battery. Once the battery reaches this threshold, it is assumed to require replacement. To achieve this, the state-of-health of each battery is estimated using the Arrhenius equation. In the analysis, each scenario underwent simulation across three prominent bus drive cycles: the Manhattan bus cycle, the New York bus cycle, and the California bus cycle. These simulations were conducted using Simulink models, which were designed to update the battery’s maximum capacity after each cycle run based on the Ampere-hour throughput. Ultimately, the cost-effectiveness was evaluated and compared in dollars per kilometer. The results indicate that distinct battery options emerge as the optimal choices for each of the considered drive cycles.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".