Control-oriented modeling and adaptive parameter estimation of a lithium ion intercalation cell

Abstract

Battery management systems using parameter and state estimators based on electrochemical models for Lithium ion cells, are promising efficient use and safety of the battery. In this thesis, two findings related to electrochemical model based estimation are presented - first an extended adaptive observer for a Li-ion cell and second a reduced order model of the Pseudo Two-Dimensional model. In order to compute the optimal control at any given time, a precise estimation of the battery states and health is required. This estimation is typically carried out for two metrics, state of charge (SOC) and state of health (SOH), for advanced BMS. To simultaneously estimate SOC and SOH of the cell, an extended adaptive observer, guaranteeing global stability for state tracking, is derived. This extended adaptive observer is based on a non-minimal representation of the linear plant and a recursive least square algorithm for the parameter update law. We further present a reduced order model of the Pseudo Two-Dimensional model, that captures spatial variations in physical phenomena in electrolyte diffusion, electrolyte potential, solid potential and reaction kinetics. It is based on the absolute nodal coordinate formulation (ANCF) proposed in [281 for nonlinear beam models. The ANCF model is shown to be accurate for currents up to 4C for a LiCoO2 /LiC6 cell. The afore mentioned extended adaptive observer is also applied to the ANCF model and parameters are shown to converge under conditions of persistent excitation..