Source-Free Dynamic Weighted Federated Transfer Learning for State-of-Health Estimation of Lithium-Ion Batteries With Data Privacy

Most existing methods for battery state-of-health (SOH) estimation rely on centralized training mode. However, in practical applications, it is difficult for a single user to collect sufficient battery degradation data to train a model. In addition, in order to protect data privacy, users are unwilling to share data, where centralized training mode is not the best choice. To overcome these barriers, a source-free dynamic weighted federated transfer (SF-DWFT) method for battery SOH estimation is proposed, which utilizes a distributed learning paradigm to combine multiple source clients to train a global model while protecting data privacy. First, a Gaussian mixture model is used to model the high-level features extracted by the client model, thus the feature representation can be shared without needing access to the source data; then, in order to reduce the effect of distribution differences between the source and target clients on the global model, a dynamic weighted federated aggregation algorithm is proposed according to their contributions, which is measured by calculating the modified Bhattacharya distance between the source and target clients and testing error. Finally, the effectiveness of SF-DWFT is verified on several battery datasets and it achieves a statistic estimation error of 1.12% on 18 650 lithium-ion battery datasets and 4.46% on NASA battery dataset.