Thermal heat flux distribution prediction in an electrical vehicle battery cell using finite element analysis and neural network
Bibliographic record
Abstract
In terms of battery design and evaluation, Electric Vehicles (EVs) are receiving a great deal of attention as a modern, eco-friendly, sustainable transportation method. In this paper, a novel battery pack is designed to maintain a uniform temperature distribution, allowing the battery to operate within its optimal temperature range. The proposed battery design is part of a main channel where a portion of cool air will pass from an inlet then exit from an outlet where a uniform temperature distribution is maintained. First, a 3-D model of a battery cell was created, followed by thermal simulation for 15C, 25C, and 35C ambient temperatures. The simulation results reveal that the temperature distribution is nearly uniform, with slightly higher values in the middle portion of the cell height. Second, using finite element analysis (FEA), it was determined that the heat flux per unit area is nearly uniform with a slight increase at the edges. Third, a machine learning model is proposed by utilizing a neural network (NN). Lastly, the heat flux values were predicted using the NN model that was proposed. The model was assessed based on statistical measures where a root mean square error (RMSE) value of 0.87% was achieved. The NN outperformed FEA in terms of time consumption with a high prediction accuracy, leveraging the potential of adopting machine learning over FEA in related operational assessments.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".