Conventionally, experiments that measure battery lifespan and SOH are adopted to evaluate performance, thereby assisting the development and evolution of novel battery systems. This kind of process, however, normally requires a long evaluation cycle, which inevitably hinders battery developers and users from efficient production, use, and optimization. On the other hand, informatics and data science have brought new opportunities to battery prognosis. These data-driven or machine-learning-based methods exhibit the great potential to unveil hidden information from the battery degradation data of early cycles and generate forecasts on future performance.

So far, most data-driven approaches for battery prognosis are focused on predicting the performance of commercial lithium ion batteries, which usually require large dataset consisting of high quality data. When exploring novel battery systems such as lithium metal batteries, however, these kinds of methods are less suitable as the dataset size as well as the data quality is rather limited. It is therefore extremely important to develop a generalizable battery prognosis method that is compatible with small datasets and can endure compromised quality data.