Comparative Analysis of Active Liquid Cooling Strategies for High-Power Lithium-Ion Battery Modules
Bibliographic record
Abstract
Thermal management plays a critical role in the performance, safety, and longevity of lithium-ion battery packs. As demand for fast-charging solutions and higher-energy-density battery packs continues to grow, advanced thermal management strategies are essential to maintain cell temperatures within safe operational limits. To address this challenge, this work investigates two prominent cooling techniques: (1) single-sided and (2) double-sided parallel cooling, to evaluate their trade-offs in thermal performance and energy density for automotive applications. A single cell model of the Samsung INR21700-50G cylindrical cell was developed using a multiscale, multidomain modeling approach in GT-ISE software. Thermal models were developed to represent two key components of the battery module: (1) thermal ribbons and (2) cylindrical cells. The thermal performance of single-side and double-side cooling strategies was evaluated at three charge rates:$1 \mathrm{C}, 2 \mathrm{C}$, and 3 C, focusing on temperature gradients, peak temperatures, and their broader implications for battery safety, performance, longevity, and safety. The results demonstrate that the double-sided cooling strategy consistently achieves superior thermal performance, achieving lower peak temperatures and narrower thermal gradients in all charge rates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".