Model-Based Approach to Long Term Prediction of Battery Surface Temperature
Bibliographic record
Abstract
Temperature has a substantial impact on the safety, efficiency, and reliability of a lithium-ion (Li-ion) battery. The objective of a battery thermal management system (BTMS) is to maintain the battery in preferred temperature range. State-of-the-art BTMS is designed to react to the measured temperature at the battery surface, whereas research suggests that the performance of BTMS will likely improve when used in conjunction with predicted temperature. This article focuses on surface temperature prediction of Li-ion batteries based on an equivalent circuit model-based approach. Particularly, this article presents theoretically sound approaches to model heat generation in the core of the battery and its propagation to its surface. Another novel aspect of the present article is that the heat generation and propagation model identification is tested in various practical scenarios, where the measurement noise is significant. Experimental results using data collected from high precision battery cycler and temperature sensor show an open-loop mean absolute error of 1.678$^{\circ }$C during a high-current discharge that lasted approximately 800 s. Using the theoretical models presented in this article, the performance battery temperature prediction error at low SNR conditions can be understood.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".