Blind and task-ware multi-cell battery management system
Bibliographic record
Abstract
Multi-Cell Battery (MCB) management and battery lifetime extension is vital for battery powered systems. This paper focuses on MCB scheduling and proposes the blind and task-ware scheduling methods to prolong MCB lifetime. The first step into designing an efficacious method to schedule MCB is using an MBC battery model for computer simulations. To do so, this research introduces an MCB model based on Peukert law and recovery effect. The effect of different time steps on MCB lifetime is studied and it is shown that there is only one optimum time step for each discharge pattern that maximizes the MCB lifetime. In the next step, blind and task-ware scheduling are introduced for MCB management. The blind method uses a neural network time step estimator to find the optimum time step for MCB at different discharge currents without any contemplation about drawn current pattern, but the task-ware MCB scheduling method considers the discharge pattern and finds the best set of solutions during a specific time interval. The simulations show that the task-ware scheduling method extends the battery lifetime on average by 31% compared to blind method.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".