Optimizing battery thermal management with phase change materials: Influence of thickness, ambient conditions, and material selection
Bibliographic record
Abstract
Effective thermal management is critical for maintaining the performance, safety, and lifespan of lithium-ion batteries, which operate most efficiently within a narrow temperature range of 20–40 °C. Exceeding this range can lead to accelerated degradation, uneven aging, and capacity loss. We numerically investigate the thermal performance of a passive battery thermal management system employing phase change materials (PCMs) for temperature regulation in cylindrical lithium-ion cells. The effects of PCM thickness (1–7 mm), ambient temperature (20–50 °C), PCM type, external convective heat transfer coefficient, and PCM fill volume (33 %, 66 %, and 100 %) are systematically examined under discharge rates ranging from 2C to 5C. The model is validated against experimental measurements of battery surface temperature under various discharge conditions, showing a mean deviation under 5 %. Simulation results show that a PCM with a melting point near 35 °C offers the best balance between temperature control, latent heat utilization, and thermal uniformity under moderate ambient conditions. While thicker PCM layers reduce peak battery temperatures, they also increase thermal resistance, leading to greater temperature gradients and underutilization of the outer PCM region. Among the tested materials, lower-melting-point PCMs are more effective at low ambient temperatures, whereas higher-melting-point PCMs perform better under elevated thermal loads. Increasing the external convective heat transfer coefficient from 5 to 20 W/m 2 K enhances surface cooling and steepens the thermal gradient, improving temperature regulation. However, it also accelerates energy dissipation to the environment, reducing PCM melt fraction and latent heat utilization. Finally, higher PCM fill volume delays thermal saturation and extends the effective cooling period, resulting in improved thermal regulation across the discharge cycle.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".