Battery Health Forecasting and Lifecycle Optimization Using Edge-AI and Federated Transfer Learning in EV Fleets
Bibliographic record
Abstract
The rapid adoption of electric vehicles (EVs) presents new challenges in accurately forecasting battery health and optimizing battery lifecycle management across diverse operational environments. Traditional centralized approaches often struggle with data privacy, latency, and generalization across heterogeneous EV fleets. This research proposes an integrated framework leveraging Edge Artificial Intelligence (Edge-AI) and Federated Transfer Learning (FTL) to enable collaborative, real-time battery health estimation without sharing raw data. By deploying lightweight AI models at the edge and fine-tuning them through federated learning across decentralized EV nodes, the proposed system ensures privacy preservation and low-latency predictions while enhancing generalization across fleet diversity. Furthermore, transfer learning mechanisms are employed to adapt pretrained battery degradation models to new vehicles with minimal labeled data, significantly reducing the cold-start problem. Experimental results demonstrate improved prediction accuracy, enhanced model convergence, and extended battery lifecycle management under real-world fleet conditions. This work contributes to sustainable EV fleet operation by combining privacy-preserving AI, energy-aware deployment, and intelligent battery management for improved reliability and cost-effectiveness.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".