State of Health Prediction for Lithium-Ion Batteries Using Partial Charging-Transformer-Based Deep Learning Models
Bibliographic record
Abstract
This study introduces a Partial Charging-Transformer model, which leverages partial charging data (3.6V-4.0V) to estimate State of Health (SOH) effectively.The proposed approach extracts key degradation-related features, including total charging time, and total current charge, which provide valuable insights into battery aging trends.A series of experiments were conducted using the NASA battery dataset, where the proposed model was trained on individual battery data and tested across different battery cells.The results demonstrated that the Partial Charging-Transformer model achieved more than 66% lower RMSE compared to conventional deep learning methods, including LSTM, Multi-Layer Perceptron, standard Transformer, and CNN-LSTM architectures.Notably, the use of partial charging data did not compromise predictive accuracy, making the approach highly practical for Battery Management Systems (BMS).Additionally, this method enhances computational efficiency by reducing data requirements while maintaining robust performance.This research highlights the potential of partial charging data in real-world battery health monitoring and demonstrates the effectiveness of transformer-based architectures in capturing battery degradation trends.The findings pave the way for efficient and scalable SOH estimation models, which are essential for optimizing battery lifespan and performance in practical applications.
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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 | 0.000 | 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".