Transformer-Based Transfer Learning for Battery State-of-Health Estimation
Bibliographic record
Abstract
The accurate prediction of batteries’ state of health has been an important research topic in recent years, given the surge in electric vehicle production. Dynamically assessing the current state of health of a battery can help predict how long the battery will last during the next discharge cycle, which is directly related to an electric vehicle’s autonomy calculations. Data-driven approaches have been successful in accurately estimating the state of health through machine learning-based models. Within this research topic, limited studies have been carried out to explore the transfer learning capabilities of these models to improve performance and reduce computational costs related to training. This paper aims to compare the performance of different machine learning models to adapt to diverse battery working conditions, as well as their transfer learning capabilities to batteries with different electrochemical compositions. A new transformer-based model is proposed for the SOH estimation problem. The results show that the proposed transformer model can improve its prediction performance through transfer learning when compared to the same model trained exclusively on the target dataset. When pre-trained on the NASA dataset and fine-tuned on the Oxford dataset, the transformer achieved an average RMSE of 0.01461, outperforming the best-performing model (an ANN with an RMSE of 0.01747) trained exclusively on the target data by 17%. On top of improving its performance, the model is also able to outperform a competing transformer model from the literature, which reported an RMSE of 0.90170 on a similar cross-composition transfer task.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".