Source-free foundation model-enabled transferable state of health estimation for lithium-ion batteries with intelligent adapter mapping

Accurate state of health (SOH) estimation of lithium-ion batteries is essential for ensuring the safe and reliable operation of battery-powered systems. Variations in battery types and operating conditions give rise to distribution discrepancies, for which various domain adaptation strategies have been proposed. However, current domain adaptation approaches typically require access to source domain information, including model parameters, model structure, and even the source data. Considering the increasing awareness of data protection and security restrictions, this work proposes a novel source-free foundation model-enabled SOH estimation framework designed for black-box scenarios, where only the access permission to a pre-trained source model and limited labeled target samples are required. First, degradation-sensitive features based on crucial voltage ranges are extracted from source batteries to construct a foundation SOH estimation model, reducing reliance on full-cycle measurements. Second, a novel intelligent adapter model is proposed to bridge the distribution gap between the source and target domains by leveraging an intermediate reference battery, enabling latent feature alignment without access to the source data or internal details of the source model. Finally, a fine-tuning strategy under limited target labels is employed for model adaptation to the target domain. Extensive experiments are conducted on multiple cells and compared with several representative domain adaptation approaches. The results demonstrate that, compared with the most competitive source-free adaptation baseline, the proposed framework achieves approximately 41% lower average RMSE and a consistently higher average R 2 of 0.9657, validating its effectiveness under label scarcity and privacy constraints.