A noise-tolerant model parameterization method for lithium-ion battery management system

A well-parameterized battery model is prerequisite of the model-based estimation and control of lithium-ion battery (LIB). However, the unexpected yet inevitable noises may markedly discount the identification of model parameters in real applications. This paper focuses on the noise-immune and unbiased model parameter identification for LIB. The signal-disturbance interface in LIB model identification is firstly analyzed by reformulating an overdetermined nonlinear system, on the premise of a cautiously-designed instrumental vector estimator. The multi-variable identification is then solved in the framework of a separable nonlinear least squares (SNLS) problem via a novel two-step method combining least squares (LS) and variable projection algorithm (VPA), to co-estimate the noise variances and unbiased model parameters. A numerical solver is further exploited for the proposed LSVPA, giving rise to a recursive and computational efficient algorithmic architecture which is favorable for online applications. The proposed method is validated with both simulations and experiments in terms of the noise tolerance and the parameterization accuracy.