Transfer Learning With Deep Neural Network for Capacity Prediction of Li-Ion Batteries Using EIS Measurement
Bibliographic record
Abstract
In this study, transfer learning (TL) technique is used in conjunction with deep neural network (DNN) to predict the capacity of lithium-ion batteries. First, the base DNN model is trained and validated based on the source dataset containing electrochemical impedance spectroscopy (EIS) measurement at temperatures of 25 °C and 35 °C. Then, the base DNN model is retrained and validated using different proportions, i.e., the first 50% and 20% of the target dataset, which contains EIS measurement at the temperature of 45 °C. This will create a new model called DNN-TL carrying the knowledge from the base model. The DNN-TL model is used to predict the second proportions, i.e., the second 50% and 80% of the target dataset considered as missing data. The maximum mean absolute percentage error (MAPE), when the first 50% and 20% of the target dataset are used for retraining DNN-TL with no fixed-layer, is found to be 0.605% and 0.999%, respectively, which indicates the accuracy of the model to estimate the capacity of batteries. The average$R$-squared of 0.9683 is achieved by DNN-TL with no fixed-layer indicating the goodness of its fit and its capability to follow the actual missing datasets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".