Chained Gaussian processes to estimate battery health degradation with uncertainties

Estimating the average health degradation of a new battery design is a crucial objective for manufacturers to estimate its value. Furthermore, to quantify financial risks, associated uncertainties should be modeled precisely. From a data-driven perspective, Gaussian process regression (GPR) is often a method of choice since it simultaneously learns complex models and naturally includes uncertainties. However, GPR methods generally rely on a stationarity assumption which imposes severe constraints on uncertainties. In this paper, we illustrate the limits of standard GPR and show that the Chained Gaussian processes, a more general framework introduced in the Machine learning community, is a useful alternative, allowing more accurate quantification of uncertainties.