An Enhanced Online Temperature Estimation for Lithium-Ion Batteries
Why this work is in the frame
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Bibliographic record
Abstract
This article presents an enhanced internal temperature-estimation method for lithium-ion batteries using a 1-D model and a dual Kalman filter (DKF). The cylindrical battery cell is modeled by a 1-D thermal model with three nodes. This model provides a more accurate representation of the temperature distribution, resulting in more detail of the temperature field. With the newly developed 1-D model, an enhanced temperature-estimation method is developed by including the internal resistance identification and SOC estimation in the temperature-estimation process. Experiments and simulations are conducted to evaluate the robustness and accuracy of the temperature estimation. The estimated temperature using the 1-D model with random initial values is compared with the surface temperature from experiments, which shows excellent robustness against random initial values. High estimation accuracy is demonstrated by the comparison between the estimated temperature field and the simulated temperature field from a high-fidelity 3-D model. Experimental results show that the DKF method provides better stability than the single Kalman filter, and the accuracy of the internal temperature estimation is improved by the equivalent thermal conductivity identification that considers the anisotropy of thermal conductivity in different directions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it