  State of health (SOH) is a crucial indicator for assessing the degradation
level of batteries that cannot be measured directly but requires estimation.
Accurate SOH estimation enhances detection, control, and feedback for Li-ion
batteries, allowing for safe and efficient energy management and guiding the
development of new-generation batteries. Despite the significant progress in
data-driven SOH estimation, the time and resource-consuming degradation
experiments for generating lifelong training data pose a challenge in
establishing one large model capable of handling diverse types of Li-ion
batteries, e.g., cross-chemistry, cross-manufacturer, and cross-capacity.
Hence, this paper utilizes the strong generalization capability of large
language model (LLM) to proposes a novel framework for adaptable SOH estimation
across diverse batteries. To match the real scenario where unlabeled data
sequentially arrives in use with distribution shifts, the proposed model is
modified by a test-time training technique to ensure estimation accuracy even
at the battery's end of life. The validation results demonstrate that the
proposed framework achieves state-of-the-art accuracy on four widely recognized
datasets collected from 62 batteries. Furthermore, we analyze the theoretical
challenges of cross-battery estimation and provide a quantitative explanation
of the effectiveness of our method.
