Progress in Gaussian Process Regression in Battery State Estimations

This paper reviews Gaussian process regression methods for battery state estimation, examining studies from two decades across sparse Gaussian process theory, Bayesian filtering, and lithium-ion battery applications. It identifies limited use of sparse Gaussian processes in battery literature and proposes sparse Gaussian process particle, extended Kalman, and unscented Kalman filter formulations addressing this gap.