Numerical Investigation of Hybrid Immersion Cooling Strategies for Battery Packs in Light Electric Vehicles
Bibliographic record
Abstract
This work investigates a new hybrid thermal management system (TMS) for light electric vehicle (LEV) battery packs that uses dielectric liquid immersion cooling, heat pipes and fins to effectively control lithium-ion battery (LIB) thermal load.Different commercial dielectric oil chemistries (Cargill DE-11772 and EF-3221, LK-STO50, and MIVOLT-DFK) are evaluated as heat transfer fluids (HTFs) and compared with air and deionised water as benchmark.Additionally, the effects of heat pipe diameters (4 mm, 6 mm, and 8 mm) and the number of fins (1, 2, 3, and 5) are analysed for two configurations: fins evenly distributed along the heat pipe and fins placed only on the upper half.A 3D steady-state CFD model is developed in Ansys 2024R2 to simulate the proposed TMS for a 4S4P (14.8V, 10 Ah) Lithium-Nickel Manganese Cobalt (NMC) battery pack.Under typical 2C discharge rate, the model examines the TMS thermal performance when simulating heat transfer with and without buoyancy effects.Buoyancy improves cooling performance, especially for viscous fluids, lowering battery, HTF, and heat sink temperatures by 20%.With modest LIB heat generation rates (up to 25 kW/m³), the TMS ensures effective cooling with minimum temperature increase.However, when reaching heat generation rates up to 100 kW/m³, the battery temperature reaches 91.13°C, revealing the system's cooling capability limitations.The study examines the effect of changing heat sink and insulation equivalent convective heat transfer coefficients.Increasing the heat sink coefficient from 10 to 100 W/m²K lowers the battery temperature from 138°C to 49°C, while increasing the insulation equivalent heat transfer coefficient from 1 to 50 W/m²K lowers battery temperature from 92°C to 46°C.Also, the effect of heat pipes diameter and fins number and vertical distribution is analysed, pointing to the design with 5 evenly distributed fins to be the best thermally performing while limiting additional TMS mass.This study shows that the hybrid TMS using heat pipes, fins, immersion cooling improves compact LEV safety, performance, and battery longevity under high-demand situations.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".