Computational analysis of preheating cylindrical lithium-ion batteries with fin-assisted phase change material
Bibliographic record
Abstract
The existing conventional vehicle transportation landscape in India is grappling with challenges stemming from extensive air pollution, health risks, surging oil prices, limited fossil fuel resources, substantial oil import expenses and energy volatility. To counter these issues, Electric Vehicles (EVs) are progressively replacing internal combustion engines, offering a promising route toward decarbonization and mitigating climate concerns. EVs rely on electric motors powered by batteries, predominantly Lithium-ion batteries (LIBs), known for their superior attributes such as low self-discharge, high energy density and extended life cycle. Nevertheless, LIB performance is significantly influenced by operating temperatures, with suboptimal conditions leading to decreased efficiency, power loss and faster aging. Addressing this, an effective Battery Thermal Management System (BTMS) becomes crucial to maintain batteries at optimal temperatures, enhancing their efficiency and safety. This study focuses on a computational analysis of passive heating systems employing Fins and Phase Change Materials (PCM) for 18650 Li-ion battery thermal management at low temperatures, with specific attention to battery module analysis. Numerical analysis using ANSYS FLUENT investigates the influence of varying PCM thickness on heat transfer, predicting temperature distribution and discussing its impact on battery output performance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".