Physics-guided semantic source free adaptation for fast-charging batteries degradation prognosis

As market demand for fast-charging grows to alleviate range anxiety, prognosing battery degradation under high-rate charging protocols becomes crucial for reliable operation. However, diverse fast-charging protocols induce varying internal polarization, causing data distribution shifts that fail standard prognosis models. While domain adaptation (DA) mitigates this issue, transmitting massive standard datasets incurs high communication and storage costs. To overcome these limitations, this work formulates degradation prognosis as a source-free DA problem and proposes a physics-guided semantic source-free adaptation (PS²FA) algorithm. PS²FA adapts the network using only a pretrained source model (i.e., the standard prognosis model) and unlabeled target data (i.e., fast-charging data), thus eliminating the need for the transmission of source data. Specifically, a rate-conditioned autoencoder projects target features into the source latent space to bridge distribution gaps. Furthermore, a physics-informed alignment mechanism constrains the adapted predictions to follow electrochemically consistent trends and physically plausible degradation evolution. Validated on MIT-Stanford and 15-min fast charging datasets, PS²FA reduces training data number by 80% and decreases prediction RMSE% by up to 52% under complex protocols, demonstrating highly efficient and reliable battery health management.