A federated transfer learning approach for lithium-ion battery lifespan early prediction considering privacy preservation

Accurately predicting the lifespan of lithium-ion batteries in early-stage is crucial for effective battery management systems. However, existing methods face challenges due to a lack of diverse training data. To tackle these issues, transfer learning has been employed to leverage data from other datasets. Nevertheless, the utilization of sensitive battery data raises privacy and security concerns. In this study, we present a novel deep learning approach to address these concerns by integrating federated learning and transfer learning techniques. At first, we propose a deep learning model that utilizes a convolutional neural network with efficient channel attention as the backbone model for early predicting battery lifespan. Secondly, a knowledge-sharing model is trained during the pretraining stage using federated learning, ensuring that original data from different sources remain undisclosed. Finally, leveraging the pretrained model, a specific model to the target domain is established through transfer learning during the customized fine-tuning stage. Experimental results underscore the efficacy of the proposed model, demonstrating improved prediction performance while simultaneously addressing data insufficiency and privacy preservation concerns. The root mean square errors of three batches are 118.0185, 26.8452, and 156.1420, respectively. Compared to both local training and conventional federated learning models, our proposed approach showcases significant performance enhancements.