Coestimation of SOC and Three-Dimensional SOT for Lithium-Ion Batteries Based on Distributed Spatial–Temporal Online Correction
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Bibliographic record
Abstract
Energy storage system based on batteries is a key to achieve a green industrial economy and the online estimation of its status is critical for the battery management system. Therefore, this article proposed a distributed spatial–temporal online correction algorithm for the state of charge (SOC) three-dimensional (3-D) state of temperature (SOT) coestimation of battery. First, the internal resistance is identified, and SOC is estimated based on the adaptive Kalman filter. Then, to improve the fidelity of electrical status estimation under the dynamic operation condition, the SOC estimation is coupled with an online restoration algorithm of distributed temperature. An improved fractal growth process is used to achieve the self-organization and convergence during the restoration of 3-D temperature distribution. Finally, to validate the fidelity of online coestimation algorithm for electrical and thermal parameters, dynamic current profiles are used. The coestimation method raises the fidelity of SOC estimation by 1.5% at most, compared with the SOC estimation algorithm without the SOT estimation. It also keeps the mean relative error of SOT estimation within 8%. Additionally, the robustness of the spatial–temporal online correction method with dual adaptive Kalman filters is validated. The result shows that the coestimation algorithm still has a good convergence performance with disturbance added.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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