State-of-charge dependent equivalent circuit model identification for batteries using sparse Gaussian process regression

Abstract

Due to their ease of implementation, equivalent circuit models (ECMs) of batteries are widely used in battery management systems. Generally, ECM parameters vary with operating conditions, thus how such parameter dependencies are addressed substantially influences the accuracy of an ECM over a wide operating range. In this paper, we identify an ECM whose parameters have nonlinear dependence on state-of-charge (SOC). By transforming the SOC-dependent ECM into a linear parameter varying (LPV) input–output model, we propose a non-parametric sparse Gaussian process regression (GPR) approach, which alleviates the difficulty of specifying parametric functional SOC-dependencies of model parameters. The proposed approach derives the posterior distributions of ECM parameters, thus is capable to provide both parameter estimates and their associated uncertainties. The computational cost over large datasets is significantly reduced by adopting the sparse GPR. The proposed approach is applied to the above LPV model with two noise model structures, i.e., white and colored noises. Identification results using experimental data illustrate the efficacy of the proposed approach. The use of colored noise enhances robustness under different noise levels, and achieves higher output prediction accuracy over experimental datasets.