Fractional-order modeling of lithium-ion batteries using additive noise assisted modeling and correlative information criterion

In this paper, the fractional-order modeling of multiple groups of lithium-ion batteries with different states is discussed referring to electrochemical impedance spectroscopy (EIS) analysis and iterative learning identification method. The structure and parameters of the presented fractional-order equivalent circuit model (FO-ECM) are determined by EIS from electrochemical test. Based on the working condition test, a P-type iterative learning algorithm is applied to optimize certain selected model parameters in FO-ECM affected by polarization effect. What's more, considering the reliability of structure and adaptiveness of parameters in FO-ECM, a pre-tested nondestructive 1/f noise is superimposed to the input current, and the correlative information criterion (CIC) is proposed by means of multiple correlations of each parameter and confidence eigen-voltages from weighted co-expression network analysis method. The tested batteries with different state of health (SOH) can be successfully simulated by FO-ECM with rarely need of calibration when excluding polarization effect. Particularly, the small value of CIC alpha indicates that the fractional-order alpha is constant over time for the purpose of SOH estimation. Meanwhile, the time-varying ohmic resistance R-0 in FO-ECM can be regarded as a wind vane of SOH due to the large value of CICR0. The above analytically found parameter-state relations are highly consistent with the existing literature and empirical conclusions, which indicates the broad application prospects of this paper. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Cairo University.