Machine Learning-Based SoC and RUL Estimation Applied to an Electric Bus Fleet
Bibliographic record
Abstract
Energy transition in transportation sector is experiencing a rapid growth. Although massive renewals of outdated vehicle fleet with Electric Vehicles (EVs), battery management remains one of its main weaknesses. For this reason, research in this field is increasingly expanding, with the target of developing technologies able to combine improved performance with reduced environmental impact. This paper explores the challenging application of Machine Learning (ML) techniques for estimating the battery State of Charge (SoC) without relying on the electrical input data, typically required by classical model-based approaches. Following the set-up and optimization phases of ML models being able to associate vehicle speed to battery SoC, discharging profiles were extrapolated and analyzed to estimate the Remaining Useful Life (RUL). The proposed ML models achieved accurate results, with performance comparable to a traditional observer, despite relying on fewer and simpler variables. These results highlight the potentialities of data-driven approaches for realizing a scalable and easier battery monitoring.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".