Prediction of PCM-Based Battery Pack Thermal Performance Using a Physics-Informed Neural Network
Bibliographic record
Abstract
A physics-informed neural network (PINN) framework is developed to predict the transient thermal behavior of a lithiumion battery pack embedded in a paraffin phase change material (PCM) with copper mesh and pipe inserts. The PINN is trained to adhere to the governing heat transfer physics of the battery-PCM-copper domains while fitting sparse temperature measurements, enabling high-fidelity prediction of temperature distributions without relying on extensive sensor data for an experimental approach or an extensive mesh for a numerical approach. Two models were trained simultaneously to capture the PCM and copper domains, demonstrating the interaction among the battery heat generation boundaries, PCM latent heat absorption through enthalpy change, and thermal conduction through the copper inserts. Validation against experimental measurements across various discharge rates demonstrates that the model can accurately reproduce temporal temperature profiles, including the cell surface temperature and the minimum PCM temperature deep within the pack, with root-mean-square errors below $1^{\circ} \mathrm{C}$. Notably, the PINN predicts thermal behavior even at locations where direct instrumentation was infeasible in previous research, highlighting its ability to monitor and detect detailed internal thermal states without being limited by instrumentation or meshing requirements. The results underscore the potential of PINN-based approaches for efficient, real-time modeling of battery thermal management systems that involve phase change and high conductive material domains.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".