Parameter Identification through Physics-Based Impedance Models Using Impedance Spectra for Lithium-Ion Batteries

Abstract

Electrochemical impedance spectroscopy (EIS) is a useful characterization tool due to its ability to separate physicochemical processes by their intrinsic time constants. EIS is extensively used to study diffusion1 and kinetic2 processes, investigate passive layer growth3, and characterize aging4 in lithium-ion batteries. However, experimental EIS data is traditionally fit using equivalent circuit models with circuit elements that are representative of the physical processes happening inside a battery. Such models may lose physical meaning and interpretability as processes and parameters are lumped together.Physics-based impedance models mathematically describe physicochemical processes based on fundamentally derived equations and are thus better alternatives for providing direct insight than their equivalent circuit analogs5. These models are often highly coupled and complex, and traditional solution methods make it difficult to solve them in a reasonable time frame suited for multi-parameter identification. Recent work on a hybrid analytical-collocation approach demonstrated significant reduction in computation time (two orders of magnitude improvement compared to commercial software), making multi-parameter estimation a more feasible optimization problem6.Estimation of parameters is an important step to using physics-based models in battery management systems (BMS) to monitor, predict, and control batteries, since such physics-based models inherently require knowledge of specific battery parameters. This work builds on the previously developed fast simulation approach for physics-based impedance models for a full cell consisting of two porous electrodes, by conducting a parameter reduction study through clustering of physical parameters. We present a method to demonstrate the identification of parameters and explore the ability to estimate parameters for the impedance spectra at different states of charge of a cell and at various states of aging.