Research on intelligent operation and maintenance of electrochemical energy storage plant based on multimodal fusion sensing technology
Bibliographic record
Abstract
In order to realize the intelligent operation and maintenance of electrochemical energy storage power station and make the working process of the power station battery more efficient, stable and safe, this paper establishes a safety monitoring system of electrochemical energy storage power station through multimodal fusion sensing technology.The multi-sensor fusion technology and multi-sensor calibration process are proposed, and the Kalman joint filter fusion algorithm is obtained based on the traditional Kalman filter extension, which fuses the collected multi-modal sensing data to realize the real-time detection of the state information of each battery of the energy storage power station.Simulation experiments are carried out to verify the reliability of the Kalman joint filter fusion algorithm, and the deviation value of this algorithm in the filter fusion processing is only 0.1426, which is lower than that of the comparative sliding average filtering algorithm.The RMSE values of X-axis and Y-axis in the motion target tracking experiments are less than those of the comparative mean drift algorithm 0.189 and 0.1412, and in the speed, they are less than those of 0.0062 and 0.0073, which are better in terms of accuracy performance.And in the application practice of battery safety monitoring system for electrochemical energy storage power station, the error between SOC estimation and actual value is less than 5% in either DST condition or UDDS condition, and the internal resistance 0 R change curve is similar to the actual value of the internal resistance, and the estimation error is less than 4%.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".