Early Prediction of the Failure Probability Distribution for Energy Storage Technologies Driven by Domain-Knowledge-Informed Machine Learning

There is a growing focus on sustainable 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 a novel experiment-stopping criterion, which can significantly reduce the required data. Our framework could revolutionize energy-storage testing, enabling the rapid development of new technologies.