An Online SOC-SOTD Joint Estimation Algorithm for Pouch Li-Ion Batteries Based on Spatio-Temporal Coupling Correction Method
Bibliographic record
Abstract
An SOC (state of charge)-SOTD (state of temperature distribution) joint estimation algorithm is established for a pouch lithium-ion battery. This method integrates the first-order RC model, distributed heat generation model, thermal resistance network model, and spatio-temporal coupling correction based on spatial restoration algorithm and dual Kalman filter (DKF). Unlike the traditional 1-D SOT estimation model, the proposed algorithm can accurately estimate the temperature distribution in the battery online by restoring the battery surface temperature distribution, calculating the battery internal temperature distribution, and jointly correcting the SOC and average internal temperature. Then, the proposed method is used for the SOC-SOTD estimation of a 25-Ah pouch battery at the ambient temperatures of 10, 20, and 30 °C under the worldwide light-duty test cycle and new european driving cycle driving cycles, and the estimated values are verified by experiment and 3-D simulation. According to the verification results, calculation errors of SOC are no more than 2.12%, and the maximum average calculation errors are 0.16 °C for the surface temperature and 0.24 °C for the internal temperature. However, the DKF is needed for the SOC-SOTD joint estimation because the single KF can only correct SOC or average internal temperature but cannot handle the inconsistency in temperature distribution.
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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 | 0.000 | 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 | 0.000 | 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 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".