Universal Data Specification and Real-Time Data Streaming Architecture for Cloud-Based Battery Management Systems
Bibliographic record
Abstract
Effective operation of a cloud-based electric vehicle (EV) battery management system (BMS) and control of associated modular multilevel inverters (MMIs) require real-time streaming of operational data. However, the existing data specifications are only suitable for hard-wired battery configuration and battery testers to store long-term historical data. This article fills this gap by introducing a standard data specification and cloud-based architecture to store and analyze operational data for implementing cloud-based BMS. The approach can simplify the implementation of cloud-based digital twinning of batteries and BMS. The proposed data specification will ease research and development in the battery platform. In addition, the specification can accommodate the configuration and control parameters of MMI-based reconfigurable battery packs and is also suitable for real-time data streaming. The effectiveness of the proposed data specification in cloud-based real-time data streaming as well as state estimation, and the provision of accommodating measurement error is demonstrated through the experimental data and analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".