Completely Decentralized Active Balancing Battery Management System
Bibliographic record
Abstract
The performance of a string of series-connected batteries is typically restricted by the worst cell in the string and a single failure point will render the entire string unusable. To address these issues, we present a decentralized battery management system with no communication requirement based on a modular multilevel converter topology with a distributed inductor and distributed controller running on a local microprocessor. This configuration is referred to as a “smart cell.” By sensing the voltage across the local distributed inductor, each smart cell is able to: first, determine its optimal switching pattern in order to minimize the output voltage ripple; and second, adjust its duty cycle to synchronize its state of charge (SOC) with the average SOC of the series string of cells. The decentralized controller is derived using the theory of Kuramoto oscillators, and the stability of a system of smart cells is investigated. We experimentally show that a system of three smart cells with their decentralized controllers can accurately synchronize the SOC while minimizing their output voltage ripple.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".