A Federated Transfer Learning Framework for Lithium-Ion Battery State of Health Estimation Based on Fast-Charging Segments
Bibliographic record
Abstract
Accurately estimating the state of health (SOH) of lithium-ion batteries using limited segments of fast-charging data is essential for effective battery management in electric vehicles. However, the task is complicated by two main challenges: insufficient training data from each target battery, requiring personalized models; and privacy risks associated with centralized data aggregation. To address these issues, this work proposes a two-stage federated transfer learning framework. In the first stage, federated learning enables multiple distributed batteries to collaboratively train a global model by sharing only model parameters, preserving privacy while learning generalized knowledge. In the second stage, this global model is fine-tuned using a small amount of local data from the target battery, resulting in a personalized model that captures individual battery characteristics. The framework is built on a lightweight convolutional neural network enhanced with an efficient channel attention mechanism, enabling accurate mapping from fast-charging segments to SOH values. Experimental results on a public fast-charging battery dataset show that the proposed method significantly outperforms both local-only models and conventional federated learning approaches without personalization. It achieves a root mean square error of just 1.13%, demonstrating its effectiveness in accurately predicting SOH, preserving privacy, and potential for real-world battery management systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".