Topology-Optimized Indirect Cooling System for Thermal Management of Cylindrical Li-Ion Battery Packs
Bibliographic record
Abstract
This study introduces an innovative thermal management system for cylindrical Li-ion battery packs, designed to control temperature rise during standard operation while maintaining a uniform temperature distribution.The proposed system employs an indirect cooling approach, utilizing channels that circulate water as the heat transfer fluid.These channels are thermally coupled to the batteries via an aluminium cold plate, which serves as both thermal spreader and structural support.Notably, the metal support integrates the cooling channels without direct battery contact, serving as conductive path for heat dissipation.The analysis focuses on a Sony 18650 cylindrical battery, with a nominal capacity of 2.7 Ah and a voltage of 3.6 V. Heat transfer performance is evaluated using Bernardi's thermal model at a C-rate of 10.To address the high weight of the metallic cold plate, a topology optimization (TO) approach is implemented to identify the material distribution that allows the battery to effectively dissipate thermal power into the heat transfer fluid while reducing the system's weight.This redesigned component achieves an 80% weight reduction compared to the baseline metal spreader, with a minimal trade-off of a 1°C increase in temperature rise.The results underscore the potential of topology optimization to generate practical designs that facilitate efficient battery thermal management systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".