Pioneering Cloud-enhanced Real-time Control of Modular-Multilevel Reconfigurable Battery Packs for Automotive Applications
Bibliographic record
Abstract
A cloud-enhanced, real-time control architecture for reconfigurable battery systems (RBS) is introduced. The proposed system leverages cloud and fog computing to optimize state estimation and control, focusing on state-of-charge. The architecture is tested with a hybrid cascaded multilevel converter and evaluated concerning latency and bandwidth in hybrid and centralized compute settings. The results indicate that cloudbased control offers promising performance, particularly in latency and efficient bandwidth use. The shortest mean and median request time could be archived with the cloud, centralized, classic control approach with 71.3 ms and 67.7 ms. Furthermore, the data compression technique could reduce the amount of data transferred by 77.1% and 64.7% for the conventional battery pack and the RBS, respectively. This research closes a gap in cloud-based BMS by comparing classic and AI-based control in distributed cloud and fog computing setups.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".