Edge of Transfer Learning-Based Long Short-Term Memory Neural Networks in the Application of Battery Surface Temperature Prediction for Electric Vehicles
Bibliographic record
Abstract
Lithium-ion (Li-ion) batteries are highly sensitive to operating conditions and temperature is one of the critical conditions that affect their performance. This paper proposes a data-driven method for the prediction of the surface temperature of Li-ion batteries so that preemptive measures could be taken to maintain the temperature within the optimum range. Long short-term memory (LSTM) based neural network is one such method that helps in prediction using sequential data. In this paper, a brief and effective comparison between general LSTM (G-LSTM) and LSTM with transfer learning (LSTM-TL) is shown for the prediction of surface temperature. Theoretically, the TL method should reduce the computational burden and improve the prediction performance and the same has been observed in our experiment. This will make the system fault-tolerant. Moreover, the wide generalization and applicability of the developed model are shown through the temperature prediction on two different batteries that were not used in the training. The experimental results demonstrate that the G-LSTM model is capable of temperature prediction with RMSE error of 1.1578$^\circ$C for battery 03 and 1.2101$^\circ$C for battery 04. This error has been further reduced by around 40% to a value of 0.5012$^\circ$C for battery 03 and 0.7480$^\circ$C for battery 04 by using the LSTM-TL.
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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".