Thermal Management of Lithium-ion Battery Modules for Electric Vehicles
Bibliographic record
Abstract
This research is particularly focused on studying thermal management of lithium-ion (Li-ion) battery modules in electric vehicles by using active, passive and hybrid active-passive methods. The thermal behavior prediction of batteries is performed by a novel electrochemical-thermal model. Different approaches such as single- and double-channel liquid cooling, pure passive by using phase change materials (PCM), and hybrid active-passive thermal management systems are investigated. Various cooling system configurations are examined to expand understanding of effect of each approach on the battery module thermal responses during a standard driving cycle. It is observed that the temperature distribution of Li-ion batteries is strongly influenced by the electrical and thermal operating conditions and simplified bulk models cannot precisely predict the thermal behavior of these batteries. Additionally, the PCM-based passive systems show advantages such as compactness and simplicity over the active liquid cooling systems. However, these systems suffer from non-uniform temperature distribution due to inherently low thermal conductivity of organic PCM. An effort has been made to enhance the thermal conductivity of a paraffin wax by adding various carbon-based nanoparticles. The results revealed that the thermal conductivity of the base PCM can be improved by about 11 times when using 10% mass fraction of graphite nanopowder. The heat transfer in the nano-enhanced PCM samples showed that the presence of nanoparticles drastically repress the natural convection in the melted nanocomposites. Among the battery thermal management systems studied, the air assisted hybrid cooling system provides the best temperature distribution uniformity in the module while keeping the batteries temperature within the safe limits. Furthermore, this work attempted to recognize the most influential parameters on the temperature distribution in the battery module. It is seen that the thickness of cooling plates and PCM layers in active and hybrid systems has a significant effect on the thermal behavior of the batteries.
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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 | 0.000 |
| 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 | 0.001 |
| 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".