A Bimodal Large–Small Model Collaborative Network for Joint Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries

Accurate predictions of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are crucial to ensure their efficient and safe operations. Existing deep learning methods are hindered by the limited quantity and diversity of datasets, leading to model overfitting and poor generalization. To this end, a bimodal large–small model collaborative network (BLSCN) is designed in this article, which seamlessly combines a pretrained large vision model (LVM) with a designed small model (SM) for joint prediction of SOH and RUL of lithium-ion batteries. Specifically, the BLSCN utilizes the LVM to extract features from the bimodal images generated by the battery data, followed by an SM for feature fusion and prediction. To promote the generalization ability of LVM on the battery data, an attention mask strategy is proposed to guide the LVM to focus on more features of interest in the images. In the SM, an elaborately designed cross-fusion module (CFM) is employed to interactively fuse bimodal image features for subsequent joint prediction of SOH and RUL. Experimental results on the public dataset demonstrate the superiority of the proposed BLSCN on joint prediction of SOH and RUL, with coefficients of determination (R2) of 0.983 and 0.942 for SOH and RUL prediction tasks, respectively.