Real-time CAN Data Acquisition and Visualization: Synerging Physical-to-Virtual (P2V) Twinning of Automotive Battery Management Systems
Why this work is in the frame
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Bibliographic record
Abstract
Controller area network (CAN) is widely used in automotive applications and has become the standard communication protocol to enable efficient communication primarily between electronic control units (ECUs) to reduce the complexity and cost of electrical wiring in automobiles through multiplexing. Towards developing the cloud-based electric vehicle battery data monitoring and digital-twinning of a battery management system (BMS), this paper introduced an online CAN data acquisition and visualization technique from an automotive grade BMS of NXP®<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup>. Python-based CAN data processing tool is developed to process the raw data from the NXP® BMS and an open-source platform Grafana<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> is utilized together with the InfluxDB for visualization of the time-series data in real-time from a battery module containing 14 SAMSUNG 21700 lithium-ion battery cells. Each of those elements is implemented through the Docker container platform to become a standardized unit called a container. Besides presenting the detailed architecture of the data acquisition and visualization platform and the python-based data processing tool, this paper demonstrated the capability of the proposed architecture through examples of visualizing individual cell voltage, current, and temperature in real-time and their applications and utility in implementing cloud-based BMS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it