Numerical study on sensitivity analysis of factors influencing liquid cooling with double cold‐plate for lithium‐ion pouch cell
Bibliographic record
Abstract
Structural and flow parameters have a substantial effect on the thermal and hydraulic performance of a lithium-ion battery and cooling system. In this study, a computational fluid dynamics model is developed for double cold-plate based liquid cooling for a 20 Ah lithium-ion pouch cell, and then validated based on experimental data. An orthogonal test consisting of single factor and multi-factor analysis is designed to obtain the sensitivity of four factors, including the inlet coolant temperature, inlet coolant volume flow rate, number of cooling channels, and maximum channel width that influence the thermal behavior of the pouch cell. The multi-objective optimization for minimizing maximum temperature, maximum temperature difference and average pressure drop using genetic algorithm is conducted subjected to constraints of operating conditions for optimum battery performance. Based on the multi-objective optimization, the obtained minimized values for maximum temperature (Tmax), maximum temperature difference (ΔTmax) and average pressure drop (ΔPavg) are 25.25°C, 0.22°C and 48.76 kPa. The minimized objective functions are obtained at the inlet coolant temperature of 25°C, inlet coolant volume flow rate of 240 mL/min, number of cooling channel of 10 and maximum channel width of 1.70 mm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".