A federated transfer learning framework for lithium-ion battery state of health estimation based on fast-charging segments

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.