Early prediction of the failure probability distribution for energy-storage driven by domain-knowledge-informed machine learning

There is a growing focus on new energy sources and storage systems. The challenge with such emerging systems is their need to be warrantied for around 15 years with just a year of early testing. This requires accurate data extrapolation and estimation of the failure distribution. Physics-based approaches can be overwhelmed by the complexity of degradation, and pure data-driven approaches are inherently unable to extrapolate beyond the testing data. Here, we propose a framework for a hybrid approach for technology-agnostic customizations of a Gaussian process for stochastic and domain-knowledge-informed failure-distribution predictions. We equip the Gaussian process with customized non-stationary kernels, heteroscedastic noise models, and prior mean functions to allow for accurate extrapolation with high accuracy. Furthermore, we minimize testing time with an experiment-stopping criterion, which can significantly reduce the required data. Our framework could revolutionize energy-storage testing, enabling the rapid development of new technologies.