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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".