Personalized Federated Transfer Learning Based on Global Synthetic Data for Battery State of Health Estimation

Accurately predicting the state of health (SOH) of lithium-ion batteries is crucial for optimizing usage and extending battery lifespan. In the era of the Internet of Things (IoT), collaborative modeling represents a pivotal trend in future SOH estimation research. However, privacy constraints induce data isolation among battery organizations, limiting collaborative progress. Federated learning (FL) enables multi-party collaboration while preserving data privacy, but it faces the inherent challenge of data heterogeneity. To address this, we propose a novel personalized federated transfer learning (PFTL) framework to enable secure collaboration and build a reliable SOH estimation model in data heterogeneity scenarios. We develop the Residual Adaptive Kolmogorov-Arnold Network (RAKAN) as the predictive model within the framework, improving the base performance of local client models. Our framework constructs global synthetic data as a core element and achieves effective aggregation among local models through the global synthetic data-guided cross-client alignment mechanism. Additionally, we design a dynamic weighted aggregation strategy based on feature-focus similarity, allowing the global model to learn more generalized feature-focus patterns. This framework can further refine the global model to obtain personalized SOH estimation models tailored to the characteristics of each client. Experimental results confirm that our method achieves robust and accurate SOH estimation results while ensuring data privacy.