Principal component analysis preprocessing with Bayesian networks for battery capacity estimation

Bayesian networks (BNs) are a common data-driven approach for representing and reasoning in the presence of uncertainty. Inference in a BN can quickly become intractable as the complexity of the network increases, specifically in the number of nodes and the number of states for each node. We demonstrate the benefit of preprocessing cyclic time-series measurements using principal component analysis (PCA), evaluating the technique with the BN to perform diagnostics on a set of lithium-ion batteries that have undergone repeated charging/discharging cycles. The results show how PCA preprocessing can result in simpler Bayesian network models than those from the raw data while still achieving higher accuracy.