Effect of Heat Transfer Coefficients in Forced Convective Cooling of Batteries on the Sustained Performance of PCMs
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Bibliographic record
Abstract
Effective thermal management of lithium-ion batteries (LIBs) is necessary to sustain phase stability in an optimal temperature range of 15 to 45 C, which ensures longevity and prevents thermal runaway under repeated highfrequency discharging-charging cycles.This study focuses on determining the optimal heat transfer coefficient (h) required to facilitate complete solidification during the charging after the full melting of phase change materials (PCMs) during high-rate discharging for effective and continuous use of PCMs.Utilizing the Newman-Tiedemann-Gu-Kim (NTGK) model, the performance of the SAMSUNG ICR 18650-26J battery is studied.The cell is encased by a copper shell, followed by the addition of the PCM, and then further encapsulated by another copper shell.The present study evaluated three PCMs such as n-Heneicosane, OM42, and n-Docosane and a range of heat transfer coefficients (h) from 20 W/mK to 500 W/mK for the thermal management of cells.It is found that increasing the PCM thickness from 3 mm to 4 mm reduces the maximum cell temperature from 53.19 C to 51.94 C during a 5C discharge at 40 C ambient temperature, however, resulting in lower PCM utilization, making 3 mm as the optimal PCM thickness.At an ambient temperature of 35 C, n-Heneicosane remains in the liquid state, whereas n-Docosane maintained better thermal regulation and complete solidification, demonstrating its suitability for moderate ambient conditions.Under harsh conditions (40C), increasing the convective heat transfer coefficient to 500 W/mK during charging allows n-Docosane to solidify within 750 s fully, ensuring effective thermal management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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